# https://github.com/njanakiev/folderstats
# https://janakiev.com/blog/python-filesystem-analysis/
# https://networkx.org/documentation/stable/
from google.colab import drive
drive.mount('/content/drive')
%cd /content/drive/My Drive/IDPCode/
Mounted at /content/drive /content/drive/.shortcut-targets-by-id/1d2RlPHkcJvE2Q0NDVZjeNW8VcVz3h_WL/IDPCode
!pip install xlrd
!pip install spacy
!pip install gensim
!pip install ipython-autotime
!pip install pickle5
!pip install textacy
!pip install spacy-langdetect
!pip install pandarallel
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import pandas as pd
import numpy as np
import pickle5 as pickle
import gensim
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# show execution time of cells
%load_ext autotime
# CHANGE PATH FOR THE DATASET FOLDER
DATA_DIRECTORY='/Users/farukcankaya/FC/GoogleDrive/AllPapers'
time: 402 µs
# Utility functions
def remove_dollar_sign(text):
'''
While drawing title count distribution, mathplotlib throws an error.
Because some titles have dollar sign('$'). This method is used to clear the sign.
'''
text = str(text).replace('$', '\$')
return text
import re
import string
import textacy
from textacy import preprocessing
def clean_text(text):
'''
Eliminates links, non alphanumerics, and punctuation.
Returns lower case text.
'''
# Convert to string
text = str(text)
# Remove non-ascii
text = text.encode('ascii', 'ignore').decode('ascii')
# Remove links
text = re.sub('(?:(?:https?|ftp):\/\/)?[\w/\-?=%.]+\.[\w/\-?=%.]+', '', text)
# Remove E-Mail addresses
text = re.sub(r'\b[a-z]+@[a-z]+\b', r'', str(text))
# Remove non-alphanumerics
text = re.sub('\w*\d\w*', ' ', text)
# Remove punctuation and lowercase
text = re.sub('[%s]' % re.escape(string.punctuation), ' ', text.lower())
# Remove newline characters
text = re.sub("\n", r' ', text)
# Remove text in square brackets
text = re.sub(r'\[.*?\]', '', text)
# Remove words containing numbers
text = re.sub(r'\w*\d\w*', '', text)
# https://textacy.readthedocs.io/en/0.10.1/_modules/textacy/preprocessing/normalize.html
text = textacy.preprocessing.normalize.normalize_whitespace(str(text))
# replace words with less than 2 characters
text = re.sub(r'\b[a-z]{1,2}\b', r'', str(text))
return text
def replace_text(text, replacement_texts=REPLACED_WORDS):
for i in replacement_texts:
text = re.sub(r'\b'+str(i)+'\b', '', text)
return text
import spacy
nlp = spacy.load('en')
def lemmatizer(text, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):
sent = []
doc = nlp(text)
# for word in doc:
# sent.append(word.lemma_)
sent = [token.lemma_ if token.lemma_ not in ['-PRON-'] else '' for token in doc if token.pos_ in allowed_postags]
return " ".join(sent)
# Lemmatization, remove pronouns.
def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):
"""https://spacy.io/api/annotation"""
texts_out = []
for sent in texts:
texts_out.append(lemmatizer(" ".join(sent), allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']))
return texts_out
def remove_stopwords(text):
clean_text = []
for word in text.split(' '):
if word not in stop_list and (len(word) > 2):
clean_text.append(word)
return ' '.join(clean_text)
def display_topics(model, feature_names, no_top_words, no_top_topics, topic_names=None):
count = 0
for ix, topic in enumerate(model.components_):
if count == no_top_topics:
break
if not topic_names or not topic_names[ix]:
print("\nTopic ", (ix + 1))
else:
print("\nTopic: '",topic_names[ix],"'")
print(", ".join([feature_names[i] for i in topic.argsort()[:-no_top_words-1:-1]]))
count += 1
def word_count(text):
return sum([i.strip(string.punctuation).isalpha() for i in text.split()])
from IPython.core.display import display, HTML
def print_html(data):
display(HTML('<div style="width: 90%">'+data+'</div>'))
def print_lemmatized(text, colorized_ignored=False, ignore_type='allowed'):
allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']
doc_lemma = nlp(text)
lemms = []
poses = []
#colors = ['#f88f88','#DA553F','#045E5B','#731425','#F61E9E','#f44f44','#ff0','#0ff','#f0f']
import random
r = lambda: random.randint(0,255)
colors = ['#%02X%02X%02X' % (r(),r(),r()) for i in range(30)]
for token in doc_lemma:
if token.pos_ not in poses:
poses.append(token.pos_)
print(poses)
for token in doc_lemma:
if (ignore_type=='allowed' and token.pos_ not in allowed_postags) or (ignore_type=='stop_word' and token.is_stop):
ignored_color = '#FF8983' if colorized_ignored else colors[poses.index(token.pos_)]
lemms.append('<span style="color: #fff; padding: 0 10px; background-color: '+ignored_color+'"> '+token.lemma_+' </span>')
else:
lemms.append(token.lemma_)
print_html('allowed_postags: '+' '.join(['<span style="color: #fff; padding: 0 10px; background-color: '+colors[allowed_postags.index(i)]+'"> '+i+' </span>' for i in allowed_postags]))
print_html('available: '+' '.join(['<span style="color: #fff; padding: 0 10px; background-color: '+colors[poses.index(i)]+'"> '+i+' </span>' for i in poses]))
print_html(' '.join(lemms))
import os
def get_file_name(file_path):
f = os.path.basename(file_path)
return f.replace('.pdf', '')
def run_in_parallel(dt, function, n_cores=8):
df_n = np.array_split(df, n_cores)
pool = Pool(n_cores)
df = pd.concat(pool.map(func, df_n))
pool.close()
pool.join()
return df
time: 995 ms
The dataset in 'All_Papers_In_Plain_Text_TIKA.pkl' contains extracted text page by page per document. Each document has one row and has columns as much as they need in the dataset. Any datapreprocessing step is not applied to data in 'All_Papers_In_Plain_Text_TIKA.pkl'. It is not even normalized. It has many documents that have just one page. Our purpose here was to test the accuracy value against different datapreprocessing steps as simple as we can. Afterwards, we will do further tests by normalizing the data. So to summerize:
# dt = pd.read_pickle('en_cleaned_mallet_stop_words_lemmatized_all_pages_separetely.pkl') # https://drive.google.com/file/d/1wOnaUYVSu-vI0tQr9xe_gjCLKcapX1bd/view?usp=sharing
dt = pickle.load(open('en_cleaned_mallet_stop_words_lemmatized_all_pages_separetely.pkl', "rb"))
time: 26.1 s
dt['cleaned_page_1'].iloc[4724]
'shareholder income taxes and the relation between earnings and returns dan dhaliwal university arizona and university auckland merle erickson university chicago oliver zhen university notre dame abstract the purpose this study investigate whether and how shareholder level taxes affect earnings response coefficients ercs our tests indicate that when the tax rate dividends increases ercs decrease for firms with high levels dividend yield and whose marginal investor likely individual for firms with high levels share repurchase yield and whose marginal investor likely individual increase dividend tax rate has discernible effect ercs these results are consistent with the notion that the tax penalty dividends relative capital gains reduces the earningsreturn relation keywords dividend earnings response coefficient institutional ownership shareholder taxes jel descriptors impts sur revenu des actionnaires relation entre bnfices rendements condens les auteurs demandent les impts auxquels sont assujettis les actionnaires influent sur les coefficients raction aux bnfices comment sexerce cette influence miller modigliani ont montr quen labsence dimperfections march prsence dune politique dinvestissement dtermine les dividendes sont pas pertinents dautres termes politique dividende dune socit ninfluerait pas sur valeur ils ont galement soulign par ailleurs que diffrence dans limposition des dividendes celle des gains capital pouvoir lier politique dividende dune socit valeur mme lorsque politique dinvestissement est dtermine les auteurs tudient cette possibilit contemporary accounting research vol fall caaa accepted greg waymire with input from ken klassen and gordon richardson appreciate comments and suggestions from bruce johnson ken klassen associate editor gordon richard son editor bill schwartz greg waymire associate editor and two anonymous referees merle erickson acknowledges financial support from the graduate school business the university chicago'
time: 1.78 ms
dt['en_cleaned_page_1'].iloc[4724]
'shareholder income taxes and the relation between earnings and returns dan dhaliwal university arizona and university auckland merle erickson university chicago oliver zhen university notre dame abstract the purpose this study investigate whether and how shareholder level taxes affect earnings response coefficients ercs our tests indicate that when the tax rate dividends increases ercs decrease for firms with high levels dividend yield and whose marginal investor likely individual for firms with high levels share repurchase yield and whose marginal investor likely individual increase dividend tax rate has discernible effect ercs these results are consistent with the notion that the tax penalty dividends relative capital gains reduces the earningsreturn relation keywords dividend earnings response coefficient institutional ownership shareholder taxes jel descriptors impts sur revenu des actionnaires relation entre bnfices caaa accepted greg waymire with input from ken klassen and gordon richardson appreciate comments and suggestions from bruce johnson ken klassen associate editor gordon richard son editor bill schwartz greg waymire associate editor and two anonymous referees merle erickson acknowledges financial support from the graduate school business the university chicago'
time: 1.72 ms
dt['en_cleaned_page_1'].iloc[680]
'accounting horizons american accounting association vol accounting faculty experiences with practice oriented fellowships susan martin and jeffrey wilks synopsis describe our experiences with practice oriented fellowships along with survey data gathered from other full time accounting faculty who participated similar fellowships between and across broad set sponsoring organizations including the securities and exchange commission sec the financial accounting standards board fasb public accounting firms and other organizations our survey results identify commonalities faculty members backgrounds fellowship administration and logistical issues surrounding the transition into fellowships the nature the work experience during the fellowship transitions back academia following the fellowships and faculty members evaluations their fellowship experience reporting the issues costs and benefits from participating practice oriented fellowships hope faculty will understand more about these opportunities and more likely participate practice oriented fellowships also hope that this paper helps universities and sponsoring organizations understand better the needs and concerns faculty members who are considering whether participate these fellowships keywords fellowships sabbaticals professional leaves accounting faculty susan krische associate professor american university roger martin associate professor the university virginia and jeffrey wilks associate professor brigham young university greatly appreciate the helpful comments from dana hermanson editor and the two anonymous reviewers well our faculty colleagues from around the country who shared their practice fellowship experiences with participating our survey also thank the sec and the fasb for enabling participate their fellowship programs and particularly the individuals those organizations who were gracious with their time and expertise finally thank our respective universities including the university illinois the case professor krische for the flexibility and support that made possible for part the practice fellowship experience supplemental materials can accessed clicking the link submitted october accepted july published online january corresponding author susan krische email krische mailto krische'
time: 2.46 ms
# Import
from pandarallel import pandarallel
# Initialization
pandarallel.initialize()
INFO: Pandarallel will run on 8 workers. INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers. time: 50.4 ms
# dt = Dataset Tika
dt = pd.read_pickle('All_Papers_In_Plain_Text_TIKA.pkl') # https://drive.google.com/file/d/1mF88eFEKtni3Sr8jYo2Yo6-rqIunbMgi/view?usp=sharing
time: 7.43 s
col_page = [col for col in dt if col.startswith('page') and not col.endswith('_wc')]
col_page_wc = [col for col in dt if col.startswith('page') and col.endswith('_wc')]
time: 2.86 ms
for page in col_page:
text_series = dt[page].parallel_apply(clean_text)
dt['cleaned_' + page] = text_series
dt['cleaned_' + page + '_wc'] = text_series.parallel_apply(word_count)
time: 8min 18s
def remove_non_english_sent(text):
doc = nlp(text)
return ' '.join([sent.text for sent in doc.sents if sent._.language["language"] == 'en'])
def document_lang(text):
doc = nlp(text)
return doc._.language["language"]
time: 1.03 ms
import en_core_web_sm
nlp = en_core_web_sm.load()
#https://pypi.org/project/spacy-langdetect/
from spacy_langdetect import LanguageDetector
nlp.add_pipe(LanguageDetector(), name="language_detector", last=True)
time: 1.31 s
for page in col_page:
dt['en_cleaned_' + page] = dt['cleaned_' + page].parallel_apply(remove_non_english_sent)
time: 13h 20min 54s
dt.to_pickle('en_clean_content_separately.pkl')
time: 52.4 s
REPLACED_WORDS_EXCEL = pd.read_excel('data/StopWords/removed_words.xlsx')
REPLACED_WORDS = REPLACED_WORDS_EXCEL[REPLACED_WORDS_EXCEL['Reasonable?']==True]
REPLACED_WORDS.head(10)
print(f'Number of words: {len(REPLACED_WORDS_EXCEL)}')
print(f'Number of approved words: {len(REPLACED_WORDS)}')
REPLACED_WORDS.head(10)
REPLACED_WORDS = REPLACED_WORDS['Removed Words'].values
print(REPLACED_WORDS)
Number of words: 289 Number of approved words: 76 ['framework for the analysis' 'testingic auditing' 'review process' 'journal accounting and public policy' 'the accounting review' 'accounting forum' 'critical perspectives accointing' 'accounting and business research' 'accounting auditing accountability journal' 'doi' 'new york' 'managementothing' 'winter' 'accounting and finance' 'copyright' 'account public policy' 'accounting research and policy making' 'november' 'accounting horizons' 'june' 'ybare' 'aos' 'fdi' 'int tax public finance' 'european accounting review' 'management accounting research' 'jel classification' 'the role big' 'research managerial accounting learning' 'managerial auditing journal' 'autumn' 'february' 'issnmanagement accounting research' 'peer reviews' 'disclosuresnge' 'mcb university press' 'may' 'available online' 'sarbanesoxley act' 'rev account stud' 'december' 'july' 'fall' 'contemporary accounting research' 'march' 'january' 'vale louis goldberg' 'april' 'vol' 'aaaj' 'review accounting studies' 'critical perspectives acconting' 'sarbanes oxley' 'american accounting association' 'methodological biasesax' 'critical prespectives accounting' 'september' 'accounting organizations and society' 'critical perspectives accounting' 'chen' 'international tax and public finance' 'critical perspecives accounting' 'springer science business' 'international journal accounting information systems' 'pin' 'form' 'summer' 'fdicia' 'critical perspective accounting' 'journal accounting and economics' 'british accounting review' 'october' 'the british accounting review' 'abstract' 'critical perspectivres accounting' 'august'] time: 30.7 ms
# Extracted from PDF metadata:subject. Most of them contains journal names.
REPLACED_WORDS = ['option repricing',
'tax based accounting system',
'internal audit',
'modified audit opinions',
'confidence',
'pcaob',
'delegation',
'accounting research and policy making',
'the effects fee pressure',
'conservatism',
'cost capital',
'auditor testing strategies',
'form',
'investment banking',
'fdi',
'critical perspectives accounting',
'accounting organizations and society',
'earnings forecasts',
'accounting horizons',
'diluted earnings per share',
'contemporary accounting research',
'int tax public finance',
'cfo turnover',
'hypothesis testing',
'managerial auditing journal',
'securitization income',
'research managerial accounting learning',
'fair value',
'book income adjustment',
'endogeneity',
'incentive contracts',
'advertising',
'transfer prices',
'european accounting review',
'copyright',
'the british accounting review',
'instrumental variables',
'evaluation the effect',
'auditors risk assessments',
'debt contracting',
'corporate annual reports',
'stock offering',
'critical perspectives acconting',
'sfas',
'derivative financial instruments',
'low balling',
'comparative evaluation belief',
'implied volatility',
'professional qualifications',
'loan loss provision',
'moral hazard',
'compensation consultants',
'abstract',
'loss profit mispricing',
'methodological biasesax',
'accruals',
'chen',
'international tax and public finance',
'management accounting research',
'financial targets',
'memo structure',
'accounting recognition',
'audit fees',
'analyst research',
'brokerage research',
'doi',
'communication',
'audit quality',
'relative performance evaluation',
'audit risk',
'auditor evaluation evidence obtained',
'industry concentration',
'capital markets research and accounting',
'private information',
'terminal values',
'cooperation',
'auditor industry specialization',
'strategic expenditures',
'framework for the analysis',
'october',
'stock options',
'loss profit predictability',
'vol',
'analysts',
'audits for fraud',
'experimental economics',
'organizational structure',
'governance',
'persistence',
'subspaces',
'partial observability model',
'market efficiency',
'accountability',
'book tax conformity',
'learning',
'journal accounting and public policy',
'springer science business',
'the role big',
'peer reviews',
'february',
'agency costs',
'capital investment',
'internal control',
'employee stock options',
'auditor tenure',
'substantive',
'market reaction',
'accounting accruals',
'investor recognition hypothesis',
'estimation',
'pin',
'cash bonuses',
'analysts recommendations',
'information discovery',
'earnings announcements',
'reporting discretion',
'voluntary disclosure',
'vale louis goldberg',
'disclosure',
'peer group',
'uncertainty',
'january',
'future cash flows',
'executive compensation',
'the structure profit sharing',
'thresholds',
'manipulation',
'real activities',
'financial information',
'information content',
'industry specialization',
'internal control assessment',
'auditors',
'november',
'analytical procedures',
'review process',
'ceo compensation',
'voluntary disclosures',
'managementothing',
'financial reporting',
'aggregation',
'critical perspecives accounting',
'sarbanesoxley act',
'credibility',
'asset securitizations',
'impairment',
'information risk',
'acquisitions',
'usefulness earnings',
'japanese managers',
'implicit deposit insurance and deposit',
'seasoned equity offerings',
'autumn',
'independent',
'expenditures',
'management compensation',
'director elections',
'taxation',
'depreciation method choice',
'cost accounting',
'available online',
'behavioral finance',
'accounting auditing accountability journal',
'earnings response coefficients',
'financial reporting quality',
'double entry',
'economic conditions',
'ifrs',
'improving analytical procedures case',
'analysis the relative',
'critical perspectivres accounting',
'defence the survey',
'earnings based anomalies',
'british accounting review',
'accounting choices',
'the accounting review',
'taxes',
'review accounting studies',
'rev account stud',
'december',
'corporate risk taking',
'firm performance',
'momentum',
'post earnings announcement drift',
'new york',
'journal accounting and economics',
'audit workpapers',
'account public policy',
'product market competition',
'earnings guidance',
'march',
'insider trading',
'corporate advisors',
'issnmanagement accounting research',
'tax noncompliance',
'earnings forecast',
'may',
'stock market',
'aos',
'the information environment and the',
'agency model',
'capital investments',
'august',
'testingic auditing',
'april',
'internal control over financial reporting',
'accounting and finance',
'residual income model',
'earnings quality',
'materiality',
'june',
'compensation',
'asymmetric timeliness',
'jel classification',
'accounting forum',
'summer',
'risk',
'firm year measure',
'time budgets',
'investment policy',
'security analysts',
'sarbanes oxley',
'entrenchment',
'audit committees',
'forecast errors',
'complexity',
'aaaj',
'american accounting association',
'agency theory',
'september',
'risk management behavior',
'earnings surprises',
'financial graphs',
'disclosuresnge',
'board directors',
'ybare',
'capital markets',
'fdicia',
'accounting conservatism',
'divestitures',
'firm size',
'mcb university press',
'earnings dilutionr share',
'earnings management',
'information asymmetry',
'july',
'internal controls',
'regulation',
'outsourcing',
'straight line depreciation',
'auditor choice',
'accounting losses profits',
'decision making',
'performance measurement',
'valuation',
'sox',
'critical perspectives accointing',
'auditing market',
'experimental research method',
'reputation',
'accounting and business research',
'preparers conclusion',
'legal liability',
'management forecast accuracy',
'shielding',
'earnings',
'informativeness book tax differences',
'management forecasts',
'verification',
'management guidance',
'accelerated depreciation',
'winter',
'securitizations',
'competition',
'critical prespectives accounting',
'earnings persistence',
'corporate democracy',
'timely loss recognition',
'critical perspective accounting',
'legislative policy',
'corporate governance',
'book tax differences',
'international journal accounting information systems',
'fall',
'board director compensation',
'privatization',
'managerial perceptions',
'litigation',
'asset measurement']
time: 1.43 ms
for i in REPLACED_WORDS:
matched_index = dt.index[dt['en_cleaned_page_1'].str.match(i)]
print(str(len(matched_index)) + " -- " + i)
#dt.iloc[matched_index]['en_cleaned_page_1'].values[2]
0 -- framework for the analysis 0 -- testingic auditing 0 -- review process 0 -- journal accounting and public policy 1014 -- the accounting review 170 -- accounting forum 0 -- critical perspectives accointing 33 -- accounting and business research 1 -- accounting auditing accountability journal 110 -- doi 0 -- new york 0 -- managementothing 0 -- winter 283 -- accounting and finance 19 -- copyright 38 -- account public policy 0 -- accounting research and policy making 0 -- november 428 -- accounting horizons 1 -- june 0 -- ybare 0 -- aos 0 -- fdi 266 -- int tax public finance 120 -- european accounting review 230 -- management accounting research 0 -- jel classification 0 -- the role big 0 -- research managerial accounting learning 138 -- managerial auditing journal 0 -- autumn 0 -- february 0 -- issnmanagement accounting research 0 -- peer reviews 0 -- disclosuresnge 0 -- mcb university press 0 -- may 273 -- available online 1 -- sarbanesoxley act 14 -- rev account stud 0 -- december 0 -- july 0 -- fall 5 -- contemporary accounting research 1 -- march 0 -- january 0 -- vale louis goldberg 0 -- april 46 -- vol 19 -- aaaj 0 -- review accounting studies 0 -- critical perspectives acconting 2 -- sarbanes oxley 106 -- american accounting association 0 -- methodological biasesax 0 -- critical prespectives accounting 0 -- september 338 -- accounting organizations and society 0 -- critical perspectives accounting 1 -- chen 157 -- international tax and public finance 0 -- critical perspecives accounting 1 -- springer science business 0 -- international journal accounting information systems 0 -- pin 4 -- form 0 -- summer 0 -- fdicia 0 -- critical perspective accounting 0 -- journal accounting and economics 70 -- british accounting review 0 -- october 225 -- the british accounting review 26 -- abstract 0 -- critical perspectivres accounting 0 -- august time: 846 ms
s = "old string oldstring boldstring bold"
s = re.sub(r'\bstring\b', '*', s)
print(s)
old * oldstring boldstring bold time: 640 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
for page in col_page:
dt['en_cleaned_accounting_replaced_' + page] = dt['en_cleaned_' + page].parallel_apply(replace_text)
time: 1min 49s
stop_words_data1 = open("data/StopWords/StopWords_Auditor.txt", "r").read().lower().split()
stop_words_data2 = open("data/StopWords/StopWords_Currencies.txt", "r").read().lower().split()
stop_words_data3 = open("data/StopWords/StopWords_DatesandNumbers.txt").read().lower().split()
stop_words_data4 = open("data/StopWords/StopWords_Generic.txt").read().lower().split()
stop_words_data5 = open("data/StopWords/StopWords_GenericLong.txt").read().lower().split()
stop_words_data6 = open("data/StopWords/StopWords_Geographic.txt").read().lower().split()
stop_words_data7 = open("data/StopWords/StopWords_Names.txt").read().lower().split()
time: 7.81 ms
mallet_stop_words_en = open("data/StopWords/MALLET_en.txt", "r").read().lower().split()
mallet_stop_words_fr = open("data/StopWords/MALLET_fr.txt", "r").read().lower().split()
time: 4.97 ms
print(f'{len(set(stop_words_data1))} => StopWords_Auditor.txt')
print(f'{len(set(stop_words_data2))} => StopWords_Currencies.txt')
print(f'{len(set(stop_words_data3))} => StopWords_DatesandNumbers.txt')
print(f'{len(set(stop_words_data4))} => StopWords_Generic.txt')
print(f'{len(set(stop_words_data5))} => StopWords_GenericLong.txt')
print(f'{len(set(stop_words_data6))} => StopWords_Geographic.txt')
print(f'{len(set(stop_words_data7))} => StopWords_Names.txt')
print(f'{len(set(mallet_stop_words_en))} => MALLET English Stopwords')
print(f'{len(set(mallet_stop_words_fr))} => MALLET French Stopwords')
8 => StopWords_Auditor.txt 190 => StopWords_Currencies.txt 116 => StopWords_DatesandNumbers.txt 121 => StopWords_Generic.txt 570 => StopWords_GenericLong.txt 199 => StopWords_Geographic.txt 11905 => StopWords_Names.txt 523 => MALLET English Stopwords 126 => MALLET French Stopwords time: 4.02 ms
print(len(stop_words_data1) + \
len(stop_words_data2) + \
len(stop_words_data3) + \
len(stop_words_data4) + \
len(stop_words_data5) + \
len(stop_words_data6) + \
len(stop_words_data7))
print(len(mallet_stop_words_en))
print(len(mallet_stop_words_fr))
14336 524 126 time: 936 µs
stop_words_data2 = list(filter(lambda a : a != "|", stop_words_data2))
stop_words_data3 = list(filter(lambda a : a != "|", stop_words_data3))
stop_words_data6 = list(filter(lambda a : a != "|", stop_words_data6))
stop_words_data7 = list(filter(lambda a : a != "|", stop_words_data7))
time: 3.63 ms
stop_words_data = stop_words_data1 + \
stop_words_data2 + \
stop_words_data3 + \
stop_words_data4 + \
stop_words_data5 + \
stop_words_data6 + \
stop_words_data7 + \
mallet_stop_words_en + \
mallet_stop_words_fr
time: 721 µs
# TODO:!!!!
# Find accounting related stop words
#LIST_OF_ACCOUNTING_RELATED_STOP_WORDS = ['accounting','firms','research','audit','financial','paper','information','les','des','que','dans','sur','par','qui','est','aux','une']
LIST_OF_ACCOUNTING_RELATED_STOP_WORDS = []
LIST_OF_ACCOUNTING_RELATED_STOP_WORDS = LIST_OF_ACCOUNTING_RELATED_STOP_WORDS + stop_words_data
time: 662 µs
from gensim.parsing.preprocessing import STOPWORDS
stop_list = STOPWORDS.union(set(LIST_OF_ACCOUNTING_RELATED_STOP_WORDS))
time: 2.19 ms
mallet_fr = pd.DataFrame(data=mallet_stop_words_fr, columns=['Stop Word'])
mallet_fr.to_excel('mallet_french_stopwords.xlsx', engine='xlsxwriter')
# https://drive.google.com/file/d/1JMpcfriApigcV54q4ZPOE7GX8iwNgGBI/view?usp=sharing
mallet_en = pd.DataFrame(data=mallet_stop_words_en, columns=['Stop Word'])
mallet_en.to_excel('mallet_english_stopwords.xlsx', engine='xlsxwriter')
# https://drive.google.com/file/d/1op134plHcerwUds8E44Lw9gsI316Pus3/view?usp=sharing
time: 244 ms
s = ['accounting','firms','research','audit','financial','paper','information','les','des','que','dans','sur','par','qui','est','aux','une']
ss1x = pd.DataFrame(data=s, columns=['Stop Word'])
ss1x.to_excel('accounting_stopwords.xlsx', engine='xlsxwriter')
# https://drive.google.com/file/d/1gZfPtIQyQ82u12ScEvEndMMujfrnh8Gd/view?usp=sharing
ss1x = pd.DataFrame(data=stop_list, columns=['Stop Word'])
ss1x.to_excel('ALL_COMBINED_STOP_WORDS.xlsx', engine='xlsxwriter')
ss2 = pd.DataFrame(data=set(LIST_OF_ACCOUNTING_RELATED_STOP_WORDS), columns=['Stop Word'])
ss2.to_excel('accounting_stopwords.xlsx', engine='xlsxwriter')
ss2 = pd.DataFrame(data=set(REPLACED_WORDS), columns=['Removed Words'])
ss2.to_excel('removed_words.xlsx', engine='xlsxwriter')
time: 62.6 ms
for page in col_page:
dt['en_cleaned_accounting_replaced_stop_words_' + page] = dt['en_cleaned_accounting_replaced_' + page].parallel_apply(remove_stopwords) # uses 'stop_list' variable
time: 1min 25s
dt['en_cleaned_page_1'].head(10)
0 accounting horizons american accounting association vol the effect system characteristics stage adoption and experience institutional explanations for erp systems choice joseph ugrin synopsis existing studies have found linkage between institutional factors and systems adoption but the literature has overlooked important question when the decision implement system influenced institutional factors this paper the author argues that decisions adopt systems are not influenced equally institutional factors and examines how system characteristics moderate the effects institutional factors adoption decisions specifically decisions adopt enterprise resource plan ning hereafter erp systems the results the study reveal that institutional factors such mimicry peers compliance with industry norms and coercion from powerful entities influence erp adoption decisions the results also show that the effects institutional factors adoption decisions are amplified when participants have not already adopted erp system the benefits the system are difficult quantify and the system enhances organizational interaction throughout the supply chain through this paper the author contributes the institutional theory literature exploring con ditions that enhance the salience institutional factors and contributes accounting information systems literature investigating the boundaries the influence insti tutional factors adoption keywords institutional theory technology acceptance systems adoption isomorphism erp data availability please contact the author for copy the data introduction ensible use technology can give organizations competitive advantage this may especially true for enterprise resource planning hereafter erp systems that are capable transforming organizational processes through integration and automation markus and anis erp systems often provide firms positive return hayes but that not lways the case erp systems are costly and have often resulted inconsistent economic returns oseph ugrin assistant professor kansas state university hanks marcus odom john pearson jacob rose john morris stacy kovar and dave andrus for their constructive eedback and guidance thanks also ronnie jia for his assistance with related research proposal presented amcis and the reviewers the aaa information systems section midyear meeting research symposium for pro iding direction finally thanks dan stone and the reviewers accounting horizons for their invaluable assistance submitted august accepted july published online november corresponding author joseph ugrin email jugrin 1 accounting horizons american accounting association vol memorial this the last the ten memorials that associate editor stephen zeff has solicited reviewed and edited his successor associate editor charge memorials will robert colson william cooper innovator fighter and scholar rajiv banker and robert kaplan introduction illiam wager cooper was born july birmingham alabama where his father was bookkeeper and later distributor for anheuser busch bills family moved chicago when was three years old his father owned chain gasoline stations but lost them during the great depression with his father ill health and family revenue bill left high school after his sophomore year work whatever jobs could find these included spotting pins bowling alleys caddying golf courses and even professional boxing where his record was wins losses and draws while hitchhiking the golf course one day bill met eric kohler principal with arthur andersen who became his life long mentor kohler loaned him money enroll non degree student the university chicago bill liked the academic atmosphere and took the college entrance examinations intending become physical chemist during his studies kohler asked him review the mathematics used patent infringement suit after cooper found errors the mathematics used the plaintiffs engineers andersen hired him part time during the school year and full time the summer this awakened coopers interest accounting and switched his major economics while chicago bill became good friends with political science student herbert simon cooper graduated phi beta kappa with his rajiv banker professor temple university and robert kaplan professor emeritus harvard university published online march corresponding author rajiv banker email banker cooper repaid some his debt kohler editing two volumes cooper and ijiri cooper ijiri and previts memory kohler following his death mailto banker 2 accounting horizons vol december auditor changes and the pricing seasoned equity offers yongtae kim and myung seok park synopsis while prior literature examines the role auditors the pricing initial public offerings little known about the effect auditor changes the pricing seasoned offers our examination seasoned equity offerings shows that companies switching auditors prior the offerings underprice their offers more than companies without changes while provide evidence issuers opportunistic accounting cisions that are consistent with their opinion shopping behavior the positive associa tion between underpricing and auditor changes suggests that switchers bear net cost compared non switchers from practical standpoint our findings alert com pany considering change its auditor prior new equity issue keywords auditor change seasoned equity offering underpricing data availability data are publicly available from sources identified the paper introduction managers public company who consider issuing seasoned equity face multitude whether change their auditors prior the issuance managers may have various reasons for changing auditors such obtain higher quality service avoid disagreements over generally accepted accounting principles gaap the decision change auditors likely send signal the market and because the issuers underwriter attempts gauge market sentiments the underwriter may well adjust the offer price due change auditors this paper explore the effects auditor changes the underpricing seasoned equity offerings seos previous literature initial public offerings ipos posits role for the auditor reducing the information inequality between the issuer and the underwriter willenborg menon and williams beatty balvers beatty and ritter empirically better auditors appear reduce information problems and thus the underpricing that issuers face ipos menon and williams beatty balvers however seos are different from ipos and therefore whether auditor plays yongtae kim assistant professor santa clara university and myung seok park assistant professor virginia commonwealth university are grateful two anonymous reviewers and especially robert lipe editor for many insightful comments and suggestions and workshop participants the american accounting association annual meeting for their comments professor kim acknowledges financial support provided the presidential research grant santa clara university submitted october accepted july corresponding author yongtae kim email 3 4 accounting horizons american accounting association vol the fasbs dissenting opinions michael bradbury and julie harrison synopsis this paper provides commentary the results content analysis dissenting opinions financial accounting standards board fasb standards during the fasb issued financial accounting standards half these standards contained dissenting opinions identify and classify dissenting opinions based whether the arguments are conceptual conceptual framework related non framework related non conceptual scope due process examine whether the types and frequencies arguments change over time response the development the fasbs conceptual framework and provide commentary the role these opinions and the usefulness analyzing them for research and practice our main finding from our analysis that conceptual arguments are the most frequently used the dissenting opinions both before and after the introduction the conceptual framework however note that many the arguments raised while conceptual nature are not from the conceptual framework suggest this indicates either need for the conceptual framework language more widely used the authors dissenting opinions and the emergence new conceptual arguments that may relevant for future revisions the conceptual framework keywords fasb dissenting opinions conceptual framework issenting opinions are common the setting financial accounting standards board fasb standards however there little analysis these decisions the objective this paper analyze the content dissenting opinions contained statements financial accounting standards fas issued during the period december june and comment the role these opinions and the usefulness analyzing them for research and practice this regard modify the taxonomies yen hirst and hopkins and bennett michael bradbury professor massey university and julie harrison senior lecturer the university the authors thank the anonymous reviewers for their guidance also thank kimberley crook bryan howieson robert larson christopher nobes brendan oconnell kevin stevenson stephen zeff and participants the university exeter seminar series the european accounting conference and the iaaer conference for their helpful comments this paper special thanks the massey university research fund for its support submitted july accepted january published online january corresponding author julie harrison email mailto 5 accounting horizons american accounting association vol associations between internal and external corporate governance characteristics implications for investigating financial accounting restatements william baber lihong liang and zinan zhu this study documents changes the corporate governance characteristics and changes associations between governance charac teristics during the period further investigation indicates that corporate governance characteristics and the probability financial accounting restatement are related but not finally demonstrate the importance considering interactions among governance characteristics when investigating governance determinant financial reporting quality these results inform thinking about empirical investigations associations between corporate governance characteristics and financial accounting restatements keywords corporate governance governance regulation accounting restatements jel classifications introduction ecent accounting failures high profile firms and attendant speculation that the lack oversight contributed substandard and misleading financial reporting inspired series academic studies designed investigate associations between accounting restatements and governance characteristics many these studies focus accounting restatements indicator financial reporting quality early empirical studies restatements document statistically significant associations between financial restatements and specific governance characteristics presumed indicate independent oversight the financial reporting william baber professor georgetown university lihong liang assistant professor syracuse university and zinan zhu assistant professor the national university singapore the authors appreciate comments from divya anantharaman david harris chris jones sok hyon kang naomi soderstrom mary sullivan amy tong jeffrey weinstein david ziebart and seminar participants george mason university syracuse university and the aaa annual meeting mid atlantic region thank xiaolu for her research assistance submitted february accepted october published online june corresponding author william 6 accounting horizons vol american accounting association doi management earnings forecasts review and framework eric hirst lisa koonce and shankar venkataraman this paper which view management earn ings forecasts specifically categorize earnings forecasts having three componentsantecedents characteristics and consequencesthat roughly corre spond the timeline associated with earnings forecast evaluating management earnings forecast research within the context this framework render three con clusions first forecast characteristics appear the least understood component earnings forecastsboth terms theory and empirical researcheven though the component over which managers have the most control second much the prior research focuses how one forecast antecedent characteristic influences forecast consequences and does not study potential interactions among the three components third much the prior research ignores the iterative nature management earnings forecaststhat forecast consequences the current period influence antecedents and chosen characteristics subsequent periods implications for researchers educa tors managers investors and regulators are provided introduction anagement earnings forecasts are voluntary disclosures that provide information about expected earnings for particular firm such forecasts represent one the key volun tary disclosure mechanisms which managers establish alter market earnings ectations preempt litigation concerns and influence their reputation for transparent and accurate eporting management earnings forecasts are influential they have been shown affect stock rices pownall analysts forecasts baginski and hassell and bid ask spreads coller and yohn the purpose this paper twofold first provide organizing ramework which evaluate research management earnings forecasts this framework haracterizes management forecasts having three componentsantecedents characteristics and eric hirst professor and lisa koonce professor both the university texas austin and hankar venkataraman assistant professor the georgia institute technology thank the department accounting the university texas for funding for this project and steve baginski john assell greg miller dave ziebart editor and two anonymous referees for helpful comments submitted august accepted january published online september corresponding author lisa koonce email 7 accounting horizons american accounting association vol audit firms from longer tenure aloke ghosh subprasiri jackie siriviriyakul baruch collegecuny synopsis offer economic explanation for why audit firms oppose mandatory firm rotation using innovative sample that overcomes sample selection biases find that fees for big audit firms increase noticeably over the audit firms tenure contrast fees for non big audit firms decline tenure lengthens using audit report lag proxy for audit find that audit cost declines over the audit firms tenure and this decline our results indicate that big engagements become more profitable earn quasi rents over time which may explain why big audit firms are opposed firm but not partner rotation whether non big auditors earn any quasi rents remains doubtful our findings suggest need better monitor auditor independence and audit judgments when tenure long especially for big auditors because economic bonding between the audit firm and client tends increase over time jel classifications keywords audit tenure audit effort audit fees profitability introduction concerns that lengthy audit firm client relationships undermine auditor independence skepticism and objectivity and may erode audit quality doty legislators and regulators have tried impose term limits these term limits have been the subject active lobbying the accounting profession especially big audit firms according big audit firms donated more than million national political campaigns and spent another million federal lobbying efforts opposing regulation aimed strengthening auditor independence bazerman and moore why are audit firms opposed firm rotation but not audit partner rotation the most prevalent explanation that clients benefit from superior audit quality rendered over longer audit firm tenure regardless partner tenure bell causholli and knechel chen lin and lin ghosh and moon mansi maxwell and miller myers myers and omer johnson khurana and reynolds contrast posit that audit firms thank scott bauguess mingcherng deng jere francis marshall geiger chris hogan robert holthausen yong gyu lee kathleen hanley steve lilien liz peltier wagner tom ray bill ruland terry shevlin norm strauss and the participants the securities and exchange commission sec workshop and especially linda myers editor two anonymous referees and steve lustgarten for their comments and constructive suggestions are greatly indebted doug carmichael for his insights this topic and for his comments related issues also thank ted joyce for his comments potential estimation biases from data truncation problems editors note accepted linda myers under the senior editorship teri lombardi yohn submitted october accepted november published online february the aftermath penn central equity funding and other corporate scandals the staff the subcommittee report accounting and management the senate government operations committee published study the metcalf report the accounting profession and recommended mandatory audit firm rotation however report issued american institute certified public accountants aicpa group the following year reached different conclusions about the need for reform the cohen commission also considered mandatory rotation but recommended against regulating audit firm tenure congress again considered mandatory rotation but eventually directed the general accounting office gao study the effects rotation the gao report concludes that percent the big audit firms were opposed rotation response recent initiative audit firm rotation the public company accounting oversight board pcaob received more than comment letters primarily from audit firms almost universally opposing mandatory rotation doty audit partner rotation introduced required the lead audit partner rotate off engagement after seven years aicpa under the sarbanes oxley act sox house representatives both the lead and concurring partners are required rotate off engagement after five years under the securities and exchange commission sec rules lead audit partners must sit out for another five years following rotation before returning the same engagement laurion lawrence and ryans 8 accounting horizons american accounting association vol privately owned firms bryan brockbank oklahoma state university karen hennes the university oklahoma synopsis managers have discretion over the day the week and the time day that are filed with the securities and exchange commission sec they can elect file containing negative news times with lower market attention several recent studies find various stock price related incentives for strategic filing behavior for firms with publicly traded equity this study use filings from firms with public filings mostly from public debt but non public equity ownership from explore the strategic timing behavior firms without stock price motivations consistent with strategic disclosure find evidence that bad news are more likely filed when market attention lower just after markets close fridays and after markets close fridays even absent stock price concerns cross sectionally find more strategic timing for firms with external scrutiny from analysts and credit rating agencies despite the sophistication those market participants within firms also find more strategic timing around public debt offerings time increased market scrutiny for private firms lastly use edgar download data provide supporting evidence that strategic timing bad news disclosures does reduce short window stakeholder attention the bad news overall provide evidence that privately owned firms behave strategically within the reporting rules despite the lack stock price incentives keywords form strategic disclosure disclosure timing private firms introduction orporate disclosures involve choices about the form tone and timing the disclosure well the actual content considering market participants limited attention and finite processing abilities existing theory suggests that positive news should announced during trading hours while negative news should announced after markets close maximize firm value mitigating the negative market reaction bad news gennotte and trueman the empirical evidence from firms with public equity generally supportive finding that bad news more likely filed after markets close whether earnings announcements patell and wolfson damodaran bagnoli clement and watts dehaan shevlin and thornock management forecasts doyle and magilke filings segal and segal however the majority incentives proposed for strategically timing negative news disclosures are based delaying minimizing adverse stock price reactions for litigation insider trading purposes this study examine the timing sec filings for less studied sample privately owned firms those with public disclosures but non public equity ownership explore whether firms strategically time bad news disclosures even where there are equity price concerns more specifically use sample non earnings announcement filings from privately owned firms examine whether firms without stock price based incentives are still more likely file bad news during the following low market attention windows after markets close fridays after markets close fridays further explore whether increased scrutiny non equity holders influences this strategic filing behavior address two main research questions appreciate helpful comments from the editor the reviewers and workshop participants the university oklahoma the aaa southwest regional meeting and the aaa annual meeting thank burch kealey for help retrieving the filing submission times and michael drake for the edgar download data used this study editors note accepted lynn rees under the senior editorship teri lombardi yohn submitted may accepted october published online february all privately owned firms our sample are filing with the sec for public debt for exceeding size thresholds million total assets and accredited non accredited shareholders firms with sec reporting obligations are excluded from both the scope and sample this study 9 accounting horizons american accounting association vol the use and facilitators and obstacles the evolution the audit profession michael alles synopsis big data the most important developments management practice today with mckinsey global institute arguing that will fundamentally change business forbes states that the market for big data will reach billion growing times faster than the overall information technology market given the growing significance big data business tool this paper considers the extent which big data will embraced the audit profession and how that usage will evolve over time put forward the hypothesis that auditors cannot stray too far from the practices their clients since their credibility with and respect those clients are the basis the value added that they provide hence big data becomes essential business tool then inevitably will have the same impact auditing albeit perhaps later and with more muted reaction analysis also indicates that american and international auditing standards technological advances and market forces are some the facilitators and obstacles that will determine the use big data auditors and that will shape how that usage will evolve over time keywords auditing big data disruptive innovation erp introduction ckinsey global institute states that big data will fundamentally change not just business but the world mckinsey global institute cautions however that companies and other organizations and policy makers need address considerable challenges they are capture the full potential big data given both its promise and the challenges big data pose businesses general this paper examines the drivers the use big data the audit profession and the facilitators and obstacles for how that use will evolve the near future drivers refer the exogenous forces that will make the use big data historical inevitability and strategic necessity opposed entirely michael alles associate professor rutgers the state university new jersey newark thank eric cohen miklos tuttle associate editor and anonymous referee for their comments this paper submitted january accepted january published online february corresponding author michael alles email alles mailto alles Name: en_cleaned_page_1, dtype: object
time: 14.1 ms
for page in col_page:
dt['en_cleaned_accounting_replaced_stop_words_lemmatized_' + page] = dt['en_cleaned_accounting_replaced_stop_words_' + page].parallel_apply(lemmatizer)
time: 1h 26min 33s
dt.to_pickle('en_cleaned_accounting_replaced_stop_words_lemmatized_all_pages_separetely.pkl')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 18min 7s
May lead to confusion, think about that again!!!
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
def stem_text(text):
word_list = []
for word in text.split(' '):
word_list.append(stemmer.stem(word))
return ' '.join(word_list)
for page in col_page:
dt['cleaned_stop_words_lemmatized_stemmed_' + page] = dt['cleaned_stop_words_lemmatized_' + page].apply(stem_text)
dt.to_pickle('cleaned_stop_words_lemmatized_stemmed_all_pages_separetely.pkl')
text = dt['cleaned_stop_words_page_1'].head(1)[0]
lemmatized_text = dt['cleaned_stop_words_lemmatized_page_1'].head(1)[0].split()
stemmed_text = dt['cleaned_stop_words_lemmatized_stemmed_page_1'].head(1)[0].split()
stemm_words = []
for i in range(len(stemmed_text)):
if stemmed_text[i] == lemmatized_text[i]:
stemm_words.append(stemmed_text[i])
else:
stemm_words.append(stemmed_text[i]+'<span style="background-color: #FF8983">'+(lemmatized_text[i])[len(stemmed_text[i]):]+'</span>')
print_html(' '.join(stemm_words))
print_lemmatized(' '.join(LIST_OF_ACCOUNTING_RELATED_STOP_WORDS), colorized_ignored=False, ignore_type='stop_word')
print_lemmatized(dt['en_cleaned_page_1'].head(1)[0])
print_lemmatized(dt['en_cleaned_stop_words_page_1'].head(1)[0])
print_lemmatized(dt['en_cleaned_stop_words_lemmatized_page_1'].head(1)[0])
dt['content_3'] = dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_1'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_2'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_3'].apply(str)
dt['content_10'] = dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_1'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_2'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_3'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_4'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_5'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_6'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_7'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_8'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_9'].apply(str) + ' ' \
+ dt['en_cleaned_accounting_replaced_stop_words_lemmatized_page_10'].apply(str)
time: 18.8 s
dt3c = pd.DataFrame(dt, columns=['file_path', 'Author', 'date', 'title', 'content_3', 'content_5', 'content_10'])
time: 6.86 s
dt3c['content_3_lang'] = dt3c['content_3'].parallel_apply(document_lang)
time: 12min 25s
dt3c['content_5_lang'] = dt3c['content_5'].parallel_apply(document_lang)
dt3c['content_10_lang'] = dt3c['content_10'].parallel_apply(document_lang)
time: 43min 15s
dt3c.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15751 entries, 0 to 15750 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15751 non-null object 1 Author 7830 non-null object 2 date 15525 non-null object 3 title 14074 non-null object 4 content_3 15751 non-null object 5 content_5 15751 non-null object 6 content_10 15751 non-null object 7 content_3_lang 15751 non-null object 8 content_5_lang 15751 non-null object 9 content_10_lang 15751 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 221 ms
print(dt3c['content_3_lang'].unique())
for lang in dt3c['content_3_lang'].unique():
print(f'{lang}: {list(dt3c["content_3_lang"].values).count(lang)}')
['en' 'UNKNOWN' 'sq' 'it' 'ro' 'ca' 'fr' 'es' 'pt' 'da'] en: 15193 UNKNOWN: 532 sq: 7 it: 7 ro: 5 ca: 1 fr: 3 es: 1 pt: 1 da: 1 time: 9.74 ms
unknown_lang_indexes = pd.Index([])
dt3c_copy = dt3c.copy()
for lang in dt3c['content_3_lang'].unique():
if lang != 'en':
unknown_lang_indexes = unknown_lang_indexes.union(dt3c.index[dt3c['content_3_lang'].str.match(lang)])
for lang in dt3c['content_10_lang'].unique():
if lang != 'en':
unknown_lang_indexes = unknown_lang_indexes.union(dt3c.index[dt3c['content_10_lang'].str.match(lang)])
dt3c = dt3c.drop(unknown_lang_indexes)
dt3c.info()
dt3c.reset_index(drop=True, inplace=True)
dt3c.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 15189 entries, 0 to 15750 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 7795 non-null object 2 date 14976 non-null object 3 title 13543 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object dtypes: object(10) memory usage: 1.3+ MB <class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 7795 non-null object 2 date 14976 non-null object 3 title 13543 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 239 ms
def word_count(text):
return len(re.split('\s+', str(text)))
dt3c['wc'] = dt3c['content_10'].apply(word_count)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. <>:2: DeprecationWarning: invalid escape sequence \s <>:2: DeprecationWarning: invalid escape sequence \s <ipython-input-549-8fc15d469e47>:2: DeprecationWarning: invalid escape sequence \s
time: 14.5 s
papers_has_10_words = dt3c[dt3c['wc']>1000]
papers_has_10_words = papers_has_10_words[papers_has_10_words['wc']<4000]
wc_count = papers_has_10_words['wc'].value_counts().sort_index()
print(dt3c['wc'].value_counts().sort_index())
201 2 202 3 203 1 204 3 206 1 207 2 208 2 209 2 210 3 211 1 212 2 213 2 214 1 216 3 217 1 218 4 219 3 220 2 222 2 223 3 224 1 225 1 226 1 227 1 228 2 229 2 230 1 232 3 233 1 234 1 235 5 236 3 237 2 238 2 239 2 240 3 241 4 242 4 244 1 245 2 246 4 247 1 248 6 249 2 250 1 251 1 252 3 253 1 254 4 256 2 257 3 258 4 260 2 261 3 262 2 263 2 264 1 266 1 267 3 268 5 269 4 271 2 272 4 273 2 274 1 275 7 277 1 278 4 279 1 280 2 281 2 282 2 283 5 284 2 285 1 286 3 287 3 288 2 289 2 290 1 291 3 292 1 293 2 294 2 295 3 296 5 297 3 298 3 300 3 301 1 302 1 303 2 304 3 305 1 306 3 307 2 308 1 309 2 310 2 311 2 312 2 313 1 314 2 315 2 316 3 317 1 318 2 319 1 320 4 321 4 322 2 323 2 324 3 325 2 326 1 327 2 328 1 329 3 330 3 331 5 332 2 333 3 334 5 335 2 337 3 339 1 340 1 341 2 342 2 345 1 346 2 347 3 348 4 349 1 350 2 351 2 352 3 353 2 354 3 355 2 356 1 357 1 358 1 359 4 361 2 363 2 364 2 365 4 367 3 369 5 370 1 371 1 372 1 373 3 374 2 375 1 376 2 377 1 378 2 379 1 380 5 381 2 382 1 384 3 386 3 388 2 389 1 390 1 391 1 393 2 394 4 395 5 396 6 397 4 398 1 400 4 401 1 403 2 404 1 405 1 406 7 407 1 408 1 409 1 410 3 411 1 412 2 413 1 414 1 415 3 416 1 417 2 418 1 419 2 420 1 421 2 422 1 424 2 425 1 426 2 427 1 428 1 430 3 431 1 432 4 433 2 434 3 436 1 439 2 440 3 441 5 442 1 443 1 444 1 446 2 447 1 448 2 450 4 451 3 452 1 454 4 455 13 456 1 457 2 458 3 459 1 460 1 461 4 462 4 463 1 464 2 465 3 466 1 467 1 468 5 469 1 470 2 471 2 472 4 473 3 475 1 476 1 477 1 478 2 480 1 482 1 483 3 485 2 488 2 489 1 490 2 491 1 492 2 493 1 496 1 497 1 500 1 501 3 503 1 504 4 505 1 509 2 510 2 513 1 518 1 520 4 521 1 522 1 523 1 525 1 526 1 527 1 528 1 529 2 530 4 531 1 532 3 534 1 536 3 538 2 539 1 541 1 542 1 544 1 545 2 546 1 547 1 548 1 550 3 551 1 552 2 553 1 554 1 555 1 557 1 558 2 560 1 561 1 563 3 564 2 566 1 569 1 570 1 572 1 575 2 576 1 577 2 578 1 581 1 582 1 583 1 584 1 587 1 589 1 597 1 599 1 600 1 601 1 602 1 605 1 606 1 607 1 608 2 609 1 614 1 616 1 622 1 626 1 631 1 637 2 638 1 640 1 641 1 645 1 646 1 647 1 649 1 650 1 651 1 652 1 653 1 655 1 656 1 657 1 658 2 661 1 663 1 664 1 665 2 666 1 667 1 671 1 672 1 673 2 674 1 675 1 676 1 679 1 680 2 681 1 685 1 686 1 690 1 693 1 694 1 697 2 698 2 699 2 701 2 703 1 704 3 705 1 707 2 709 1 710 1 711 2 713 3 714 3 715 1 716 2 719 1 720 1 721 2 723 1 724 1 727 2 728 2 729 1 733 2 735 1 737 2 738 1 739 1 740 1 741 2 742 1 743 1 746 1 747 1 752 1 755 1 756 1 757 1 758 2 759 4 760 3 762 1 763 1 764 1 765 8 766 1 767 1 768 1 769 1 770 4 771 4 773 2 774 2 776 1 777 4 779 3 781 3 783 2 784 3 787 1 788 1 789 1 790 3 791 2 792 1 793 2 794 2 795 2 799 2 800 1 802 1 804 3 805 2 806 2 807 2 811 3 814 1 815 1 816 1 821 1 822 2 823 2 829 2 830 1 831 1 832 1 833 2 835 2 837 1 842 1 844 1 849 1 850 3 851 2 852 1 855 2 856 1 857 1 858 1 860 2 861 1 862 2 866 1 868 4 869 3 871 2 873 1 874 1 876 1 877 3 880 1 881 2 882 1 883 2 884 1 885 1 886 2 887 2 889 1 890 4 893 1 897 2 898 1 899 1 900 2 901 1 904 2 905 1 906 1 907 3 908 1 909 1 912 2 913 2 914 2 915 1 916 1 917 1 919 3 921 1 922 1 924 5 925 2 927 1 928 1 929 1 931 2 933 1 935 1 937 2 938 4 939 1 940 2 942 1 944 3 945 2 946 1 947 1 948 1 950 2 951 6 952 2 953 3 957 1 958 3 959 1 960 2 961 1 962 4 963 1 964 3 965 2 967 1 969 1 970 1 971 1 972 1 973 5 974 1 976 2 977 1 978 2 979 2 980 2 981 2 982 3 983 1 984 4 985 3 986 1 987 2 988 4 989 2 990 1 994 2 996 3 997 3 1000 1 1001 1 1003 1 1004 1 1005 1 1007 2 1008 3 1009 3 1010 2 1011 1 1012 1 1013 1 1014 1 1015 3 1016 3 1017 1 1018 1 1019 2 1020 1 1021 2 1022 1 1023 1 1024 3 1026 2 1027 1 1028 1 1029 2 1033 1 1035 2 1036 2 1037 2 1038 2 1039 3 1040 1 1042 6 1043 2 1044 2 1045 2 1047 1 1049 1 1050 1 1051 1 1053 1 1054 1 1056 1 1059 3 1060 1 1061 2 1064 1 1066 1 1067 2 1069 1 1070 2 1071 2 1072 1 1073 4 1075 2 1076 1 1077 4 1078 1 1079 2 1082 3 1083 2 1084 3 1085 1 1086 1 1087 1 1088 5 1089 2 1090 3 1091 1 1093 2 1094 3 1095 2 1096 1 1098 2 1100 7 1101 1 1102 1 1103 3 1105 3 1107 3 1108 1 1109 1 1110 3 1111 1 1112 2 1113 1 1114 3 1115 2 1116 5 1117 1 1119 4 1120 1 1122 3 1123 3 1124 3 1125 3 1127 4 1128 4 1129 1 1130 4 1133 1 1135 2 1136 2 1137 2 1138 1 1139 1 1140 2 1141 1 1142 1 1143 6 1144 1 1145 1 1146 3 1147 1 1148 3 1149 2 1151 2 1152 2 1153 5 1154 1 1155 3 1156 1 1157 2 1159 3 1160 1 1161 1 1162 3 1163 2 1164 2 1165 1 1166 2 1167 4 1168 1 1169 3 1171 2 1172 4 1173 1 1174 3 1176 2 1177 3 1178 1 1179 2 1180 1 1181 3 1182 2 1183 3 1184 1 1185 5 1186 1 1187 1 1189 2 1190 3 1191 3 1192 1 1193 4 1195 3 1196 4 1197 3 1198 2 1199 4 1201 2 1203 4 1204 7 1205 4 1206 6 1207 4 1208 3 1209 3 1210 2 1211 1 1212 3 1213 4 1214 2 1215 2 1216 5 1217 3 1218 1 1219 3 1220 2 1221 2 1222 1 1223 1 1224 3 1225 5 1226 2 1227 2 1228 2 1229 2 1230 2 1231 3 1233 2 1234 1 1235 1 1236 2 1237 1 1238 3 1239 2 1240 2 1241 4 1242 2 1243 5 1244 3 1245 1 1246 1 1247 1 1248 3 1249 2 1250 4 1251 3 1252 3 1253 4 1254 1 1255 5 1256 1 1257 1 1258 3 1260 2 1262 6 1264 2 1265 2 1266 5 1268 1 1269 8 1270 2 1271 5 1272 3 1273 4 1274 5 1275 1 1276 1 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3679 1 3680 1 3684 1 3686 3 3687 1 3690 2 3691 2 3692 3 3694 2 3695 1 3698 1 3699 1 3701 1 3702 2 3704 3 3705 1 3706 1 3707 1 3709 1 3716 1 3718 2 3719 2 3723 1 3724 1 3727 1 3728 1 3729 1 3733 1 3734 3 3736 1 3737 1 3739 2 3741 2 3745 2 3746 1 3749 2 3751 1 3758 1 3759 2 3762 2 3763 1 3764 2 3767 2 3769 1 3770 2 3771 1 3773 1 3774 1 3775 1 3778 1 3782 1 3783 1 3784 3 3786 1 3787 1 3792 1 3794 1 3796 1 3798 1 3799 1 3802 1 3805 1 3808 1 3811 2 3813 1 3814 1 3815 2 3818 1 3819 2 3820 1 3821 1 3822 1 3828 1 3831 1 3833 1 3835 1 3836 1 3837 2 3842 1 3843 1 3844 1 3847 2 3850 1 3851 1 3855 1 3857 1 3858 1 3859 1 3860 1 3862 2 3863 1 3864 1 3865 1 3871 1 3874 1 3877 2 3878 1 3881 1 3883 1 3884 1 3895 1 3896 1 3899 1 3902 2 3903 2 3907 1 3909 1 3910 1 3912 1 3913 1 3918 1 3919 1 3922 1 3929 2 3930 1 3931 1 3935 1 3937 1 3938 1 3940 2 3947 2 3951 1 3953 1 3961 1 3963 1 3971 1 3976 1 3977 2 3978 1 3983 1 3984 1 3986 2 3987 1 3996 1 4005 1 4010 1 4014 1 4016 1 4024 1 4027 1 4034 1 4055 1 4067 1 4083 1 4101 1 4104 1 4108 1 4112 2 4116 1 4143 1 4145 1 4162 1 4171 1 4172 1 4176 1 4182 1 4185 1 4196 3 4224 1 4226 1 4231 1 4236 1 4297 1 4299 1 4327 1 4329 1 4349 1 4354 1 4453 1 4456 1 4492 1 4540 1 4584 1 4587 1 4795 1 Name: wc, dtype: int64 time: 27.4 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
with plt.style.context('ggplot'):
ax = dt3c['wc'].value_counts().sort_index().plot(
kind='bar', color='C1', title='Word Count / Document Distribution', figsize=(18, 6))
ax.set_xlabel("Word Count")
ax.set_ylabel("Number of Documents")
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 57.5 s
print(wc_count)
201 2 202 3 203 1 204 3 206 1 207 2 208 2 209 2 210 3 211 1 212 2 213 2 214 1 216 3 217 1 218 4 219 3 220 2 222 2 223 3 224 1 225 1 226 1 227 1 228 2 229 2 230 1 232 3 233 1 234 1 235 5 236 3 237 2 238 2 239 2 240 3 241 4 242 4 244 1 245 2 246 4 247 1 248 6 249 2 250 1 251 1 252 3 253 1 254 4 256 2 257 3 258 4 260 2 261 3 262 2 263 2 264 1 266 1 267 3 268 5 269 4 271 2 272 4 273 2 274 1 275 7 277 1 278 4 279 1 280 2 281 2 282 2 283 5 284 2 285 1 286 3 287 3 288 2 289 2 290 1 291 3 292 1 293 2 294 2 295 3 296 5 297 3 298 3 300 3 301 1 302 1 303 2 304 3 305 1 306 3 307 2 308 1 309 2 310 2 311 2 312 2 313 1 314 2 315 2 316 3 317 1 318 2 319 1 320 4 321 4 322 2 323 2 324 3 325 2 326 1 327 2 328 1 329 3 330 3 331 5 332 2 333 3 334 5 335 2 337 3 339 1 340 1 341 2 342 2 345 1 346 2 347 3 348 4 349 1 350 2 351 2 352 3 353 2 354 3 355 2 356 1 357 1 358 1 359 4 361 2 363 2 364 2 365 4 367 3 369 5 370 1 371 1 372 1 373 3 374 2 375 1 376 2 377 1 378 2 379 1 380 5 381 2 382 1 384 3 386 3 388 2 389 1 390 1 391 1 393 2 394 4 395 5 396 6 397 4 398 1 400 4 401 1 403 2 404 1 405 1 406 7 407 1 408 1 409 1 410 3 411 1 412 2 413 1 414 1 415 3 416 1 417 2 418 1 419 2 420 1 421 2 422 1 424 2 425 1 426 2 427 1 428 1 430 3 431 1 432 4 433 2 434 3 436 1 439 2 440 3 441 5 442 1 443 1 444 1 446 2 447 1 448 2 450 4 451 3 452 1 454 4 455 13 456 1 457 2 458 3 459 1 460 1 461 4 462 4 463 1 464 2 465 3 466 1 467 1 468 5 469 1 470 2 471 2 472 4 473 3 475 1 476 1 477 1 478 2 480 1 482 1 483 3 485 2 488 2 489 1 490 2 491 1 492 2 493 1 496 1 497 1 500 1 501 3 503 1 504 4 505 1 509 2 510 2 513 1 518 1 520 4 521 1 522 1 523 1 525 1 526 1 527 1 528 1 529 2 530 4 531 1 532 3 534 1 536 3 538 2 539 1 541 1 542 1 544 1 545 2 546 1 547 1 548 1 550 3 551 1 552 2 553 1 554 1 555 1 557 1 558 2 560 1 561 1 563 3 564 2 566 1 569 1 570 1 572 1 575 2 576 1 577 2 578 1 581 1 582 1 583 1 584 1 587 1 589 1 597 1 599 1 600 1 601 1 602 1 605 1 606 1 607 1 608 2 609 1 614 1 616 1 622 1 626 1 631 1 637 2 638 1 640 1 641 1 645 1 646 1 647 1 649 1 650 1 651 1 652 1 653 1 655 1 656 1 657 1 658 2 661 1 663 1 664 1 665 2 666 1 667 1 671 1 672 1 673 2 674 1 675 1 676 1 679 1 680 2 681 1 685 1 686 1 690 1 693 1 694 1 697 2 698 2 699 2 701 2 703 1 704 3 705 1 707 2 709 1 710 1 711 2 713 3 714 3 715 1 716 2 719 1 720 1 721 2 723 1 724 1 727 2 728 2 729 1 733 2 735 1 737 2 738 1 739 1 740 1 741 2 742 1 743 1 746 1 747 1 752 1 755 1 756 1 757 1 758 2 759 4 760 3 762 1 763 1 764 1 765 8 766 1 767 1 768 1 769 1 770 4 771 4 773 2 774 2 776 1 777 4 779 3 781 3 783 2 784 3 787 1 788 1 789 1 790 3 791 2 792 1 793 2 794 2 795 2 799 2 800 1 802 1 804 3 805 2 806 2 807 2 811 3 814 1 815 1 816 1 821 1 822 2 823 2 829 2 830 1 831 1 832 1 833 2 835 2 837 1 842 1 844 1 849 1 850 3 851 2 852 1 855 2 856 1 857 1 858 1 860 2 861 1 862 2 866 1 868 4 869 3 871 2 873 1 874 1 876 1 877 3 880 1 881 2 882 1 883 2 884 1 885 1 886 2 887 2 889 1 890 4 893 1 897 2 898 1 899 1 900 2 901 1 904 2 905 1 906 1 907 3 908 1 909 1 912 2 913 2 914 2 915 1 916 1 917 1 919 3 921 1 922 1 924 5 925 2 927 1 928 1 929 1 931 2 933 1 935 1 937 2 938 4 939 1 940 2 942 1 944 3 945 2 946 1 947 1 948 1 950 2 951 6 952 2 953 3 957 1 958 3 959 1 960 2 961 1 962 4 963 1 964 3 965 2 967 1 969 1 970 1 971 1 972 1 973 5 974 1 976 2 977 1 978 2 979 2 980 2 981 2 982 3 983 1 984 4 985 3 986 1 987 2 988 4 989 2 990 1 994 2 996 3 997 3 1000 1 1001 1 1003 1 1004 1 1005 1 1007 2 1008 3 1009 3 1010 2 1011 1 1012 1 1013 1 1014 1 1015 3 1016 3 1017 1 1018 1 1019 2 1020 1 1021 2 1022 1 1023 1 1024 3 1026 2 1027 1 1028 1 1029 2 1033 1 1035 2 1036 2 1037 2 1038 2 1039 3 1040 1 1042 6 1043 2 1044 2 1045 2 1047 1 1049 1 1050 1 1051 1 1053 1 1054 1 1056 1 1059 3 1060 1 1061 2 1064 1 1066 1 1067 2 1069 1 1070 2 1071 2 1072 1 1073 4 1075 2 1076 1 1077 4 1078 1 1079 2 1082 3 1083 2 1084 3 1085 1 1086 1 1087 1 1088 5 1089 2 1090 3 1091 1 1093 2 1094 3 1095 2 1096 1 1098 2 1100 7 1101 1 1102 1 1103 3 1105 3 1107 3 1108 1 1109 1 1110 3 1111 1 1112 2 1113 1 1114 3 1115 2 1116 5 1117 1 1119 4 1120 1 1122 3 1123 3 1124 3 1125 3 1127 4 1128 4 1129 1 1130 4 1133 1 1135 2 1136 2 1137 2 1138 1 1139 1 1140 2 1141 1 1142 1 1143 6 1144 1 1145 1 1146 3 1147 1 1148 3 1149 2 1151 2 1152 2 1153 5 1154 1 1155 3 1156 1 1157 2 1159 3 1160 1 1161 1 1162 3 1163 2 1164 2 1165 1 1166 2 1167 4 1168 1 1169 3 1171 2 1172 4 1173 1 1174 3 1176 2 1177 3 1178 1 1179 2 1180 1 1181 3 1182 2 1183 3 1184 1 1185 5 1186 1 1187 1 1189 2 1190 3 1191 3 1192 1 1193 4 1195 3 1196 4 1197 3 1198 2 1199 4 Name: wc, dtype: int64 time: 4.36 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
with plt.style.context('ggplot'):
ax = wc_count.plot(
kind='bar', color='C1', title='Word Count / Document Distribution', figsize=(18, 6))
ax.set_xlabel("Word Count")
ax.set_ylabel("Number of Documents")
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 14 s
print(len(dt3c['wc'].values))
print(min(dt3c['wc'].values))
print(max(dt3c['wc'].values))
15194 3 1501 time: 5.12 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(len(papers_has_10_words['wc'].values))
print(min(papers_has_10_words['wc'].values))
print(max(papers_has_10_words['wc'].values))
13896 201 1199 time: 38.5 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
papers_has_10_words = dt3c[dt3c['wc']==3]
papers_has_10_words['file_path'].head(5)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
3323 /Users/farukcankaya/Google Drive/AllPapers/14_EAR/113_EAR.pdf 3416 /Users/farukcankaya/Google Drive/AllPapers/14_EAR/644_EAR.pdf 3562 /Users/farukcankaya/Google Drive/AllPapers/14_EAR/657_EAR.pdf 3605 /Users/farukcankaya/Google Drive/AllPapers/14_EAR/685_EAR.pdf 3639 /Users/farukcankaya/Google Drive/AllPapers/14_EAR/719_EAR.pdf Name: file_path, dtype: object
time: 28.4 ms
dt3c = papers_has_10_words.copy()
print(dt3c.info())
dt3c.reset_index(drop=True, inplace=True)
print(dt3c.info())
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
<class 'pandas.core.frame.DataFrame'> Int64Index: 13896 entries, 0 to 15193 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 13896 non-null object 1 Author 7074 non-null object 2 date 13683 non-null object 3 title 12343 non-null object 4 content_3 13896 non-null object 5 content_5 13896 non-null object 6 content_10 13896 non-null object 7 content_3_lang 13896 non-null object 8 content_5_lang 13896 non-null object 9 content_10_lang 13896 non-null object 10 wc 13896 non-null int64 dtypes: int64(1), object(10) memory usage: 1.3+ MB None <class 'pandas.core.frame.DataFrame'> RangeIndex: 13896 entries, 0 to 13895 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 13896 non-null object 1 Author 7074 non-null object 2 date 13683 non-null object 3 title 12343 non-null object 4 content_3 13896 non-null object 5 content_5 13896 non-null object 6 content_10 13896 non-null object 7 content_3_lang 13896 non-null object 8 content_5_lang 13896 non-null object 9 content_10_lang 13896 non-null object 10 wc 13896 non-null int64 dtypes: int64(1), object(10) memory usage: 1.2+ MB None time: 977 ms
dt3c_copy2 = dt3c.copy()
non_matched_indexes = []
for i in range(0, len(dt3c['file_path'])):
if not rt['Filename'].str.match(get_file_name(dt3c['file_path'][i])).any():
non_matched_indexes.append(i)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 1min 42s
print(len(non_matched_indexes))
4180 time: 481 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(non_matched_indexes)
[1, 3, 11, 13, 14, 17, 18, 19, 20, 25, 26, 30, 33, 37, 41, 42, 43, 45, 46, 47, 49, 50, 51, 52, 53, 57, 60, 61, 64, 70, 71, 72, 73, 74, 75, 81, 96, 99, 102, 103, 106, 110, 112, 116, 117, 119, 121, 122, 123, 124, 125, 126, 135, 137, 138, 142, 144, 146, 148, 149, 151, 156, 159, 161, 162, 163, 165, 166, 167, 169, 170, 174, 178, 180, 188, 191, 197, 203, 206, 211, 215, 216, 219, 220, 221, 224, 226, 228, 229, 230, 231, 232, 233, 235, 239, 241, 242, 246, 251, 252, 253, 254, 256, 259, 261, 262, 264, 265, 267, 271, 273, 274, 275, 276, 277, 279, 280, 281, 283, 284, 285, 287, 288, 289, 295, 298, 299, 300, 302, 304, 307, 310, 311, 312, 314, 315, 316, 317, 318, 321, 327, 328, 332, 333, 334, 336, 338, 340, 344, 346, 347, 353, 354, 356, 358, 362, 364, 365, 369, 372, 373, 381, 383, 384, 386, 389, 391, 393, 394, 396, 399, 401, 402, 404, 406, 407, 408, 410, 411, 413, 416, 419, 421, 422, 423, 424, 425, 427, 428, 432, 433, 437, 438, 442, 444, 449, 452, 454, 455, 457, 458, 460, 462, 463, 465, 468, 469, 472, 474, 476, 479, 483, 486, 488, 490, 491, 492, 495, 500, 503, 504, 509, 515, 516, 518, 520, 521, 522, 523, 525, 528, 530, 531, 540, 541, 543, 546, 548, 553, 555, 556, 557, 559, 562, 563, 564, 565, 567, 568, 569, 572, 574, 575, 577, 578, 580, 582, 583, 584, 585, 588, 589, 590, 594, 596, 598, 600, 601, 602, 603, 605, 607, 608, 610, 611, 613, 614, 618, 619, 620, 621, 626, 630, 633, 634, 640, 646, 650, 651, 653, 654, 656, 658, 662, 663, 665, 666, 667, 670, 673, 676, 680, 685, 686, 687, 689, 691, 697, 699, 701, 702, 703, 704, 706, 709, 711, 714, 715, 716, 717, 720, 721, 722, 724, 725, 729, 731, 735, 736, 737, 740, 742, 754, 758, 760, 761, 764, 765, 767, 773, 775, 782, 783, 787, 790, 793, 795, 796, 797, 800, 801, 803, 806, 808, 809, 810, 811, 813, 818, 819, 822, 824, 825, 834, 835, 836, 838, 842, 843, 847, 849, 850, 851, 854, 855, 856, 858, 860, 863, 871, 874, 879, 882, 883, 885, 886, 888, 889, 894, 898, 901, 902, 904, 905, 906, 912, 917, 918, 920, 923, 924, 926, 927, 929, 934, 938, 939, 940, 946, 947, 951, 952, 954, 955, 958, 959, 964, 965, 966, 975, 985, 986, 988, 989, 991, 992, 995, 1001, 1005, 1011, 1012, 1014, 1016, 1019, 1020, 1023, 1031, 1033, 1034, 1037, 1038, 1039, 1040, 1042, 1043, 1046, 1047, 1050, 1052, 1054, 1062, 1063, 1065, 1071, 1073, 1075, 1076, 1078, 1079, 1080, 1081, 1082, 1087, 1089, 1090, 1095, 1099, 1100, 1106, 1107, 1108, 1110, 1111, 1112, 1115, 1116, 1118, 1119, 1124, 1126, 1127, 1128, 1133, 1136, 1140, 1142, 1143, 1147, 1148, 1153, 1157, 1158, 1159, 1160, 1162, 1165, 1169, 1171, 1188, 1190, 1192, 1194, 1197, 1198, 1200, 1205, 1206, 1207, 1208, 1210, 1211, 1213, 1216, 1217, 1220, 1221, 1227, 1228, 1231, 1236, 1237, 1248, 1250, 1252, 1253, 1254, 1255, 1261, 1262, 1268, 1271, 1272, 1275, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1297, 1299, 1300, 1301, 1311, 1313, 1315, 1316, 1318, 1324, 1325, 1326, 1327, 1328, 1335, 1336, 1339, 1340, 1342, 1343, 1352, 1355, 1357, 1360, 1361, 1362, 1363, 1365, 1372, 1374, 1375, 1377, 1379, 1385, 1386, 1398, 1399, 1400, 1406, 1418, 1419, 1421, 1422, 1423, 1424, 1425, 1426, 1428, 1429, 1431, 1433, 1434, 1442, 1452, 1453, 1454, 1455, 1456, 1460, 1461, 1462, 1463, 1470, 1473, 1474, 1479, 1480, 1493, 1494, 1495, 1497, 1500, 1501, 1502, 1503, 1509, 1510, 1512, 1515, 1517, 1524, 1526, 1535, 1536, 1540, 1542, 1543, 1544, 1548, 1549, 1551, 1558, 1559, 1560, 1565, 1566, 1568, 1571, 1572, 1573, 1575, 1580, 1583, 1587, 1590, 1594, 1595, 1597, 1605, 1606, 1609, 1610, 1618, 1619, 1620, 1621, 1626, 1631, 1635, 1636, 1639, 1640, 1650, 1651, 1652, 1653, 1660, 1673, 1674, 1676, 1677, 1680, 1681, 1682, 1683, 1690, 1691, 1694, 1695, 1707, 1708, 1709, 1710, 1711, 1713, 1714, 1717, 1718, 1720, 1721, 1723, 1726, 1727, 1729, 1731, 1739, 1741, 1743, 1754, 1755, 1761, 1762, 1764, 1769, 1771, 1775, 1778, 1780, 1782, 1785, 1786, 1787, 1797, 1803, 1807, 1808, 1811, 1813, 1817, 1818, 1821, 1824, 1826, 1827, 1833, 1835, 1838, 1839, 1840, 1845, 1846, 1848, 1851, 1852, 1856, 1857, 1864, 1865, 1866, 1867, 1872, 1873, 1874, 1880, 1881, 1883, 1885, 1895, 1897, 1901, 1909, 1910, 1915, 1927, 1928, 1930, 1931, 1932, 1935, 1939, 1949, 1950, 1951, 1952, 1955, 1957, 1960, 1961, 1965, 1966, 1967, 1968, 1969, 1970, 1972, 1979, 1980, 1986, 1987, 1988, 1990, 2003, 2004, 2037, 2038, 2042, 2044, 2047, 2050, 2054, 2072, 2088, 2092, 2095, 2108, 2109, 2117, 2118, 2132, 2134, 2149, 2153, 2155, 2169, 2171, 2178, 2182, 2183, 2196, 2197, 2209, 2212, 2217, 2224, 2237, 2239, 2243, 2258, 2259, 2268, 2269, 2270, 2281, 2294, 2313, 2325, 2332, 2333, 2339, 2343, 2350, 2352, 2353, 2358, 2379, 2387, 2393, 2396, 2400, 2412, 2420, 2423, 2424, 2426, 2445, 2452, 2455, 2456, 2459, 2461, 2483, 2486, 2514, 2516, 2518, 2519, 2525, 2528, 2531, 2535, 2542, 2545, 2564, 2571, 2586, 2594, 2608, 2609, 2611, 2618, 2630, 2642, 2672, 2676, 2682, 2685, 2692, 2693, 2694, 2696, 2697, 2709, 2727, 2729, 2737, 2738, 2739, 2744, 2746, 2749, 2756, 2769, 2771, 2776, 2782, 2783, 2784, 2797, 2798, 2799, 2802, 2804, 2805, 2809, 2826, 2832, 2835, 2838, 2839, 2845, 2848, 2851, 2852, 2861, 2862, 2863, 2868, 2870, 2878, 2889, 2907, 2909, 2912, 2916, 2917, 2932, 2933, 2935, 2946, 2948, 2950, 2958, 2960, 2961, 2965, 2977, 2986, 2988, 3004, 3005, 3007, 3009, 3011, 3017, 3025, 3031, 3032, 3036, 3041, 3049, 3050, 3051, 3054, 3058, 3064, 3073, 3076, 3093, 3094, 3096, 3101, 3105, 3112, 3128, 3133, 3134, 3135, 3137, 3151, 3158, 3161, 3165, 3166, 3167, 3174, 3179, 3188, 3189, 3192, 3197, 3221, 3225, 3228, 3240, 3242, 3251, 3255, 3263, 3266, 3267, 3268, 3269, 3270, 3274, 3275, 3288, 3290, 3291, 3295, 3299, 3310, 3311, 3313, 3315, 3317, 3320, 3325, 3327, 3331, 3332, 3333, 3334, 3335, 3338, 3340, 3341, 3351, 3353, 3363, 3367, 3371, 3375, 3376, 3378, 3383, 3387, 3388, 3389, 3403, 3406, 3407, 3409, 3411, 3415, 3417, 3420, 3421, 3422, 3423, 3425, 3426, 3429, 3433, 3438, 3439, 3440, 3441, 3449, 3451, 3453, 3457, 3461, 3469, 3472, 3475, 3476, 3479, 3482, 3485, 3487, 3488, 3490, 3494, 3496, 3497, 3498, 3506, 3508, 3512, 3522, 3524, 3527, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3537, 3539, 3543, 3554, 3560, 3561, 3563, 3572, 3573, 3576, 3577, 3578, 3579, 3580, 3581, 3583, 3585, 3591, 3594, 3595, 3597, 3605, 3606, 3609, 3611, 3614, 3615, 3616, 3618, 3625, 3633, 3634, 3636, 3637, 3639, 3640, 3641, 3643, 3644, 3649, 3655, 3657, 3660, 3661, 3665, 3667, 3668, 3670, 3684, 3686, 3687, 3688, 3694, 3696, 3698, 3706, 3707, 3709, 3712, 3713, 3714, 3716, 3723, 3724, 3728, 3733, 3734, 3743, 3744, 3746, 3747, 3750, 3751, 3754, 3755, 3760, 3761, 3762, 3763, 3768, 3774, 3776, 3780, 3785, 3786, 3787, 3788, 3791, 3792, 3793, 3798, 3799, 3801, 3803, 3804, 3807, 3809, 3812, 3825, 3842, 3843, 3845, 3849, 3852, 3853, 3856, 3857, 3862, 3863, 3864, 3865, 3866, 3869, 3872, 3874, 3876, 3878, 3879, 3884, 3885, 3890, 3893, 3899, 3906, 3907, 3913, 3915, 3918, 3921, 3926, 3927, 3928, 3930, 3931, 3932, 3934, 3935, 3938, 3942, 3944, 3945, 3947, 3958, 3964, 3969, 3974, 3975, 3979, 3980, 3981, 3983, 3984, 3987, 3989, 3993, 4001, 4003, 4008, 4009, 4016, 4017, 4019, 4023, 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/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.iloc[non_matched_indexes]['file_path']
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
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Drive/AllPapers/7_AAAJ/652_AAAJ.pdf Name: file_path, dtype: object
time: 239 ms
dt3c.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15219 entries, 0 to 15218 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15219 non-null object 1 Author 7808 non-null object 2 date 15006 non-null object 3 title 13571 non-null object 4 content_3 15219 non-null object 5 content_5 15219 non-null object 6 content_10 15219 non-null object 7 content_3_lang 15219 non-null object 8 content_5_lang 15219 non-null object 9 content_10_lang 15219 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 117 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c = dt3c.drop(non_matched_indexes)
time: 19.9 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 11014 entries, 0 to 15218 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 11014 non-null object 1 Author 5799 non-null object 2 date 11002 non-null object 3 title 10051 non-null object 4 content_3 11014 non-null object 5 content_5 11014 non-null object 6 content_10 11014 non-null object 7 content_3_lang 11014 non-null object 8 content_5_lang 11014 non-null object 9 content_10_lang 11014 non-null object dtypes: object(10) memory usage: 946.5+ KB time: 17.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.reset_index(drop=True, inplace=True)
dt3c.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 11014 entries, 0 to 11013 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 11014 non-null object 1 Author 5799 non-null object 2 date 11002 non-null object 3 title 10051 non-null object 4 content_3 11014 non-null object 5 content_5 11014 non-null object 6 content_10 11014 non-null object 7 content_3_lang 11014 non-null object 8 content_5_lang 11014 non-null object 9 content_10_lang 11014 non-null object dtypes: object(10) memory usage: 860.6+ KB time: 16.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
dt3c.to_pickle('en_cleaned_stop_words_lemmatized_3_5_10.pkl')
from wordcloud import WordCloud
wordcloud = WordCloud(
background_color='white',
stopwords=stop_list,
max_words=500,
max_font_size=40,
random_state=42
).generate(str(dt3c['content_3']))
print(wordcloud)
<wordcloud.wordcloud.WordCloud object at 0x132d393d0> time: 1min 5s
import matplotlib.pyplot as plt
fig = plt.figure(1)
plt.imshow(wordcloud)
plt.axis('off')
plt.show();
time: 188 ms
NUM_OF_TOPICS = 30
NUM_OF_WORDS_IN_EACH_TOPIC = 20
time: 425 µs
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation
time: 169 ms
from plotly.offline import plot
import plotly.graph_objects as go
import plotly.express as px
def get_top_n_words(corpus, n=None):
vec = CountVectorizer(stop_words=stop_list).fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
return words_freq[:n]
common_words = get_top_n_words(dt3c['content_3'], NUM_OF_WORDS_IN_EACH_TOPIC)
df2 = pd.DataFrame(common_words, columns = ['unigram' , 'count'])
fig = go.Figure([go.Bar(x=df2['unigram'], y=df2['count'])])
fig.update_layout(title=go.layout.Title(text="Top {} unigrams in the paper after removing stop words".format(NUM_OF_WORDS_IN_EACH_TOPIC)))
fig.show()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:382: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['002', 'ain', 'aren', 'bosnia', 'couldn', 'dist', 'female', 'genealogy', 'gov', 'hadn', 'hasn', 'herzegovina', 'http', 'isn', 'list_of_most_common_surnames', 'll', 'male', 'names', 'org', 'rep', 'shouldn', 've', 'wasn', 'weren', 'wiki', 'wikipedia', 'wouldn', 'www', 'yug'] not in stop_words.
time: 17.6 s
def get_top_n_bigram(corpus, n=None):
vec = CountVectorizer(ngram_range=(2, 2), stop_words=stop_list).fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
return words_freq[:n]
common_words = get_top_n_bigram(dt3c['content_3'], NUM_OF_WORDS_IN_EACH_TOPIC)
df3 = pd.DataFrame(common_words, columns = ['bigram' , 'count'])
fig = go.Figure([go.Bar(x=df3['bigram'], y=df3['count'])])
fig.update_layout(title=go.layout.Title(text="Top {} bigrams in the paper after removing stop words".format(NUM_OF_WORDS_IN_EACH_TOPIC)))
fig.show()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:382: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['002', 'ain', 'aren', 'bosnia', 'couldn', 'dist', 'female', 'genealogy', 'gov', 'hadn', 'hasn', 'herzegovina', 'http', 'isn', 'list_of_most_common_surnames', 'll', 'male', 'names', 'org', 'rep', 'shouldn', 've', 'wasn', 'weren', 'wiki', 'wikipedia', 'wouldn', 'www', 'yug'] not in stop_words.
time: 50.5 s
"""
dt3c = pd.DataFrame(dt, columns=['file_path', 'Author', 'date', 'title'])
dt3c['content_3'] = dt['en_clean_content_3_stop_words_lemma']
dt3c.info()
unknown_lang_indexes = dt.index[dt['en_clean_content_3_lang'].str.match('UNKNOWN')]
dt3c = dt3c.drop(unknown_lang_indexes)
dt3c.info()
dt3c.reset_index(drop=True, inplace=True)
dt3c.info()
"""
"\ndt3c = pd.DataFrame(dt, columns=['file_path', 'Author', 'date', 'title'])\ndt3c['content_3'] = dt['en_clean_content_3_stop_words_lemma']\n\ndt3c.info()\n\nunknown_lang_indexes = dt.index[dt['en_clean_content_3_lang'].str.match('UNKNOWN')]\ndt3c = dt3c.drop(unknown_lang_indexes)\n\ndt3c.info()\ndt3c.reset_index(drop=True, inplace=True)\ndt3c.info()\n"
time: 2.02 ms
dt3c.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 7795 non-null object 2 date 14976 non-null object 3 title 13543 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object dtypes: object(10) memory usage: 1.2+ MB time: 17.3 ms
import pyLDAvis
import pyLDAvis.sklearn
count_vectorizer = CountVectorizer(analyzer='word',
min_df=3,
stop_words=stop_list,
lowercase=True,
token_pattern='[a-zA-Z0-9]{3,}',
max_features=5000)
data_vectorized = count_vectorizer.fit_transform(dt3c['content_3'])
lda_model = LatentDirichletAllocation(n_components=NUM_OF_TOPICS, # Number of topics
learning_method='online',
random_state=0,
n_jobs = -1 # Use all available CPUs
)
lda_output = lda_model.fit_transform(data_vectorized)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:382: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['002', 'ain', 'aren', 'bosnia', 'common', 'couldn', 'dist', 'female', 'genealogy', 'gov', 'hadn', 'hasn', 'herzegovina', 'http', 'isn', 'list', 'male', 'names', 'ncipe', 'nomm', 'org', 'rdoba', 'rep', 'shouldn', 'taient', 'tat', 'tions', 'tre', 'wasn', 'weren', 'wiki', 'wikipedia', 'wouldn', 'www', 'yug'] not in stop_words.
time: 2min 54s
By default the topics are projected to the 2D plane using PCoA on a distance matrix created using the Jensen-Shannon divergence on the topic-term distributions. You can pass in a different multidimensional scaling function via the mds parameter. In addition to pcoa, other provided options are tsne and mmds which operate on the same JS-divergence distance matrix. Both tsne and mmds require that you have sklearn installed.
Dimension reduction via Jensen-Shannon Divergence &
We used tsne below. Check for others: https://nbviewer.jupyter.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb Also check to learn how LDAVis works: https://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf And check the LDAVis from the original paper: https://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf Why LDAVis presentation: https://speakerdeck.com/bmabey/visualizing-topic-models
pyLDAvis.enable_notebook()
pyLDAvis.sklearn.prepare(lda_model, data_vectorized, count_vectorizer, mds='tsne')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 12.8 s
print(count_vectorizer)
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=5000, min_df=3,
ngram_range=(1, 1), preprocessor=None,
stop_words=frozenset({'(former', '.002%.', '1990', '>', 'a',
"a's", 'aaron', 'abbey', 'abbie',
'abbott', 'abby', 'abdul', 'abdullah',
'abe', 'abel', 'abell', 'abercrombie',
'abernathy', 'abeyta', 'abigail', 'able',
'ables', 'abner', 'abney', 'about',
'above', 'abraham', 'abram', 'abrams',
'abramson', ...}),
strip_accents=None, token_pattern='[a-zA-Z0-9]{3,}',
tokenizer=None, vocabulary=None)
time: 57.5 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(data_vectorized.toarray())
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
[[0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] ... [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0] [0 0 0 ... 0 0 0]] time: 337 ms
topic_names = []
for i in range(NUM_OF_TOPICS):
topic_names.append("Topic {}".format(i+1))
time: 525 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def show_topics(vectorizer, lda_model, n_words):
keywords = np.array(vectorizer.get_feature_names())
topic_keywords = []
for topic_weights in lda_model.components_:
top_keyword_locs = (-topic_weights).argsort()[:n_words]
topic_keywords.append(keywords.take(top_keyword_locs))
return topic_keywords
topic_keywords = show_topics(vectorizer=count_vectorizer, lda_model=lda_model, n_words=NUM_OF_WORDS_IN_EACH_TOPIC)
df_topic_keywords = pd.DataFrame(topic_keywords)
df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]
df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]
df_topic_keywords['topic_theme'] = topic_names
df_topic_keywords.set_index('topic_theme', inplace=True)
df_topic_keywords.T
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
| topic_theme | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | Topic 7 | Topic 8 | Topic 9 | Topic 10 | Topic 11 | Topic 12 | Topic 13 | Topic 14 | Topic 15 | Topic 16 | Topic 17 | Topic 18 | Topic 19 | Topic 20 | Topic 21 | Topic 22 | Topic 23 | Topic 24 | Topic 25 | Topic 26 | Topic 27 | Topic 28 | Topic 29 | Topic 30 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Word 0 | internal | author | accounting | economic | professional | risk | public | change | accounting | employee | performance | investment | environmental | company | investor | disclosure | audit | corporate | analyst | tax | quality | debt | market | firm | financial | model | accounting | manager | industry | earning |
| Word 1 | control | article | research | political | accountant | decision | government | organisation | research | job | management | capital | social | report | information | information | auditor | governance | forecast | income | accounting | credit | return | result | standard | measure | social | incentive | market | accrual |
| Word 2 | information | table | account | regulation | profession | fraud | sector | organisational | paper | family | control | asset | report | financial | announcement | report | client | shareholder | earning | rate | conservatism | risk | risk | study | accounting | variable | account | contract | cost | management |
| Word 3 | process | review | journal | market | student | judgment | private | service | study | work | cost | cost | accountability | management | market | disclose | quality | director | accuracy | dividend | adoption | financial | future | effect | statement | base | critical | compensation | firm | firm |
| Word 4 | business | text | publish | regulatory | accounting | effect | policy | health | practice | trust | study | target | corporate | committee | news | investor | auditing | ownership | error | option | reporting | rating | growth | increase | report | analysis | form | information | competition | manager |
| Word 5 | auditing | publication | business | accounting | education | research | local | care | theory | culture | organization | acquisition | stakeholder | member | earning | voluntary | big | control | management | taxis | financial | bank | volatility | performance | reporting | estimate | work | trade | profit | report |
| Word 6 | technology | include | academic | economy | work | study | pension | hospital | literature | subordinate | measure | intangible | reporting | compliance | reaction | financial | report | agency | guidance | taxation | foreign | loss | equity | evidence | account | paper | perspective | agent | transfer | flow |
| Word 7 | datum | submit | include | global | study | result | benefit | climate | account | individual | strategy | equity | sustainability | issue | event | firm | study | structure | manager | avoidance | standard | equity | portfolio | measure | user | result | role | increase | product | discretionary |
| Word 8 | external | editor | finance | standard | experience | evidence | reform | emission | issue | participation | manager | project | company | list | trading | provide | firm | executive | expectation | effect | study | covenant | study | examine | base | test | society | performance | pricing | evidence |
| Word 9 | function | style | article | policy | skill | task | plan | carbon | management | commitment | organizational | flow | csr | survey | negative | market | independence | study | issue | corporate | account | financing | period | suggest | income | valuation | human | effort | segment | future |
| Word 10 | organization | write | university | financial | worker | auditor | insurance | sector | researcher | study | strategic | valuation | study | require | evidence | cost | opinion | role | research | capital | list | lender | expect | level | issue | approach | argue | cost | revenue | loss |
| Word 11 | user | figure | issue | government | group | assessment | fiscal | scheme | process | relationship | process | accounting | responsibility | public | return | study | financial | management | dispersion | pay | difference | contract | factor | research | liability | section | people | effect | competitive | income |
| Word 12 | network | format | review | institution | woman | experiment | service | gas | analysis | level | decision | market | legitimacy | requirement | analyst | capital | partner | monitor | forecasting | increase | effect | borrower | rate | literature | require | datum | process | insider | share | result |
| Word 13 | develop | submission | financial | crisis | gender | participant | revenue | institutional | approach | satisfaction | information | impairment | issue | section | firm | increase | result | manager | accurate | gain | report | banking | result | test | provide | method | concept | set | level | manage |
| Word 14 | internet | manuscript | editor | paper | individual | individual | increase | response | perspective | employer | base | invest | business | study | recommendation | content | service | managerial | optimistic | exercise | firm | cost | asset | consistent | balance | problem | action | decision | specialist | increase |
| Word 15 | activity | reference | policy | reform | public | affect | paper | patient | theoretical | fairness | resource | merger | performance | include | institutional | public | control | financial | provide | share | result | agency | evidence | relation | item | set | change | choice | specific | period |
| Word 16 | knowledge | citation | school | set | career | factor | level | provider | focus | perceive | customer | goodwill | paper | cent | study | asymmetry | concern | mechanism | consensus | policy | market | finance | trading | base | asset | accounting | practice | pay | production | study |
| Word 17 | security | number | faculty | develop | member | influence | cost | pressure | section | supervisor | activity | expenditure | practice | reporting | suggest | section | research | institutional | ability | repurchase | increase | default | effect | hypothesis | principle | study | view | market | profitability | abnormal |
| Word 18 | provide | publish | management | account | labor | suggest | expenditure | medical | work | perception | relationship | net | impact | responsibility | coverage | filing | audits | firm | optimism | cost | conservative | derivative | liquidity | pay | set | error | ethical | private | concentration | research |
| Word 19 | enterprise | paper | professor | national | status | behavior | funding | delivery | author | outsource | level | business | organisation | review | share | research | provide | monitoring | estimate | result | measure | leverage | investor | restatement | sfa | empirical | term | trading | specialization | suggest |
time: 68.7 ms
# Create Document - Topic Matrix
lda_output2 = lda_model.transform(data_vectorized)
# column names
topicnames = df_topic_keywords.T.columns
# index names
docnames = ["Doc" + str(i) for i in range(len(dt3c))]
# Make the pandas dataframe
df_document_topic = pd.DataFrame(np.round(lda_output2, 2), columns=topicnames, index=docnames)
# Get dominant topic for each document
dominant_topic = np.argmax(df_document_topic.values, axis=1)
df_document_topic['dominant_topic'] = dominant_topic
df_document_topic.reset_index(inplace=True)
df_sent_topic= pd.merge(dt3c, df_document_topic, left_index=True, right_index=True)
df_sent_topic.drop('index', axis=1, inplace=True)
df_topic_theme = df_sent_topic[['file_path', 'dominant_topic']]
def label_theme(row):
return "Topic " + str(row)
df_topic_theme['dominant_topic_theme'] = df_topic_theme['dominant_topic'].apply(label_theme)
df_topic_theme.head(15)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. <ipython-input-53-9689e41583c7>:26: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
| file_path | dominant_topic | dominant_topic_theme | |
|---|---|---|---|
| 0 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf | 8 | Topic 8 |
| 1 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/140_AH.pdf | 4 | Topic 4 |
| 2 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/611_AH.pdf | 16 | Topic 16 |
| 3 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/294_AH.pdf | 24 | Topic 24 |
| 4 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/123_AH.pdf | 17 | Topic 17 |
| 5 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/672_AH.pdf | 18 | Topic 18 |
| 6 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/394_AH.pdf | 16 | Topic 16 |
| 7 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/398_AH.pdf | 14 | Topic 14 |
| 8 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/298_AH.pdf | 0 | Topic 0 |
| 9 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/603_AH.pdf | 16 | Topic 16 |
| 10 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/152_AH.pdf | 24 | Topic 24 |
| 11 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/703_AH.pdf | 10 | Topic 10 |
| 12 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/386_AH.pdf | 23 | Topic 23 |
| 13 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/660_AH.pdf | 1 | Topic 1 |
| 14 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/131_AH.pdf | 16 | Topic 16 |
time: 9.19 s
print(lda_output.shape)
topic_sum = pd.DataFrame(np.sum(lda_output, axis = 1))
print(topic_sum.shape)
(15189, 30) (15189, 1) time: 1.75 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
column_names = ['file_path', 'content_3'] + topic_names + ['sum']
# Turn our docs_nmf array into a data frame
doc_topic_df = pd.DataFrame(data = lda_output)
# Merge all of our article metadata and name columns
doc_topic_df = pd.concat([dt3c[['file_path', 'content_3']],
doc_topic_df, topic_sum], axis = 1)
time: 5.27 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df.columns = column_names
time: 525 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df.head(1)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
| file_path | content_3 | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | Topic 7 | Topic 8 | Topic 9 | Topic 10 | Topic 11 | Topic 12 | Topic 13 | Topic 14 | Topic 15 | Topic 16 | Topic 17 | Topic 18 | Topic 19 | Topic 20 | Topic 21 | Topic 22 | Topic 23 | Topic 24 | Topic 25 | Topic 26 | Topic 27 | Topic 28 | Topic 29 | Topic 30 | sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | /Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf | adoption experience choice synopsis exist study linkage institutional factor adoption literature overlook important question decision implement influence institutional factor paper author argue decision adopt system influence equally institutional factor examine characteristic moderate effect institutional factor adoption decision specifically decision adopt enterprise resource plan ne result study reveal institutional factor mimicry peer compliance industry norm coercion powerful entity adoption decision result effect institutional factor adoption decision amplify participant adopt benefit difficult quantify enhance organizational interaction supply chain paper author contribute institutional theory literature explore dition enhance salience institutional factor contribute account information system literature investigate boundary influence insti tutional factor adoption institutional theory technology acceptance contact author copy datum introduction ensible technology organization competitive advantage enterprise resource plan system capable transforming organizational process system provide firm positive return lway system costly result inconsistent economic return constructive guidance proposal present information system section midyear meeting research symposium pro iding direction finally reviewer account horizon invaluable assistance submit accept publish online author poor mplementation poor articular firm adopt motivated econd reason couple frequent failure system author explore doption decision process account research primarily explore outcome ffect study nicolaou summary ome recent empirical research nicolaou conversely author teps primary foci examine firm adopt adoption information system investigate decade exist model technology adoption diffusion link complex etwork organizational individual social factor adoption decision ord recent research show information system institutional factor complement ystematic cost benefit analysis teo understanding effect institutional actor important adoption decision primarily base institutional factor eemingly result limited understanding contribute specific organization systematic analysis implementation firm estructure process implementation economical exist pro esse worrisome firm adopt system involve sizable nvestment system connection institutional factor financial study investigate impact institutional factor adoption examine condition institutional factor effectual paper suggest implementation implemen ation degree uncertainty identify quantify parameter benefit solution modern institutional theory suggest firm uncertainty traditional cost benefit analyse institutional factor legitimize decision study examine role institutional factor raditional decision variable expect benefit experience manager decision legitimize action include conformity stablishe norm surrender coercion powerful entity researcher include organizational behavior accounting examine issue institutional perspective lens tudy concern system doption literature recently incorporate institutional call attention institutional perspective offer researcher vantage point eptualize digital economy emergent evolve embed fragmented provi ional social production shape cultural structural force economic teo provide evidence institutional factor affect information ystem adoption decision question remain effect institutional factor doption decision institutional literature information system largely focus ffect institutional factor system adoption decision decision maker jeyariaj leave unanswered uestion firm influence institutional factor system ccounte horizon choice institutional factor influence adoption decision type system equally hat unique characteristics decision adopt highly usceptible influence institutional factor examine hese question measure relationship perception institutionalization experience stage firm reach adoption decision result mimetic normative factor influence decision adopt mimetic factor pronounce perceive benefit adopt ystem difficult quantify adoption decision maker work firm adopt normative factor pronounce perceive integrate firm supply chain paper contribute system ure broadening understand institutional factor influence institutional theory literature testing boundary institutional theory exam ning variable situation decision adopt system susceptible nstitutional influence background nstitutional theory industry organizational structure evolve autonomy social interconnection bud characterize arket innovation firm shape market create niche creativity differentiation market mature organization rise social political echanism develop mature market characterize social environment nnovation supplant legitimizing action organization attempt adopt mechanism successful organization promote social norm group gain advantage begin accumulate resource organizational stratifie institutional legitimacy rise behavior process procedure routine facilitate stability routine behavior decouple competitive measure ceremonial political influence force organizational create pressure adopt behavior similar successful entity company institutional factor rationalize process seek lleviate uncertainty examine economic institutional factor word orga ization decision base internal external variable economic factor nclude rational cost benefit analysis relative economic advantage implement tech ology industry mature rational economic base choice complement ationalize social base choice socially develop norm arise rant relied fact shape future organizational action institutional factor play organization construct process hat include perceive legitimate organization conformity establish norm ompliance coercive pressure rise organization increasingly omogeneous increase homogeneity isomorphism amplifie nstitutional pressure result great define institutionalization process innovation regular work behavior suggest e institutionalization adoption usage ccounte horizon | 0.022459 | 0.019235 | 0.000057 | 0.070427 | 0.000057 | 0.170386 | 0.000057 | 0.000057 | 0.22931 | 0.000057 | 0.2198 | 0.000057 | 0.034574 | 0.000057 | 0.000057 | 0.000057 | 0.000057 | 0.000057 | 0.000057 | 0.000057 | 0.150271 | 0.000057 | 0.018092 | 0.053282 | 0.000057 | 0.000057 | 0.000057 | 0.000057 | 0.011085 | 0.000057 | 1.0 |
time: 25.4 ms
doc_topic_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 33 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 content_3 15189 non-null object 2 Topic 1 15189 non-null float64 3 Topic 2 15189 non-null float64 4 Topic 3 15189 non-null float64 5 Topic 4 15189 non-null float64 6 Topic 5 15189 non-null float64 7 Topic 6 15189 non-null float64 8 Topic 7 15189 non-null float64 9 Topic 8 15189 non-null float64 10 Topic 9 15189 non-null float64 11 Topic 10 15189 non-null float64 12 Topic 11 15189 non-null float64 13 Topic 12 15189 non-null float64 14 Topic 13 15189 non-null float64 15 Topic 14 15189 non-null float64 16 Topic 15 15189 non-null float64 17 Topic 16 15189 non-null float64 18 Topic 17 15189 non-null float64 19 Topic 18 15189 non-null float64 20 Topic 19 15189 non-null float64 21 Topic 20 15189 non-null float64 22 Topic 21 15189 non-null float64 23 Topic 22 15189 non-null float64 24 Topic 23 15189 non-null float64 25 Topic 24 15189 non-null float64 26 Topic 25 15189 non-null float64 27 Topic 26 15189 non-null float64 28 Topic 27 15189 non-null float64 29 Topic 28 15189 non-null float64 30 Topic 29 15189 non-null float64 31 Topic 30 15189 non-null float64 32 sum 15189 non-null float64 dtypes: float64(31), object(2) memory usage: 3.8+ MB time: 17.1 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
# Remove articles with topic sum = 0, then drop sum column
doc_topic_df = doc_topic_df[doc_topic_df['sum'] != 0]
doc_topic_df.drop(columns = 'sum', inplace = True)
# Reset index then save
doc_topic_df.reset_index(drop = True, inplace = True)
#doc_topic_df.to_pickle('counter_vectorizer_LDA_8topics_stemmed.pkl')
time: 7.42 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 content_3 15189 non-null object 2 Topic 1 15189 non-null float64 3 Topic 2 15189 non-null float64 4 Topic 3 15189 non-null float64 5 Topic 4 15189 non-null float64 6 Topic 5 15189 non-null float64 7 Topic 6 15189 non-null float64 8 Topic 7 15189 non-null float64 9 Topic 8 15189 non-null float64 10 Topic 9 15189 non-null float64 11 Topic 10 15189 non-null float64 12 Topic 11 15189 non-null float64 13 Topic 12 15189 non-null float64 14 Topic 13 15189 non-null float64 15 Topic 14 15189 non-null float64 16 Topic 15 15189 non-null float64 17 Topic 16 15189 non-null float64 18 Topic 17 15189 non-null float64 19 Topic 18 15189 non-null float64 20 Topic 19 15189 non-null float64 21 Topic 20 15189 non-null float64 22 Topic 21 15189 non-null float64 23 Topic 22 15189 non-null float64 24 Topic 23 15189 non-null float64 25 Topic 24 15189 non-null float64 26 Topic 25 15189 non-null float64 27 Topic 26 15189 non-null float64 28 Topic 27 15189 non-null float64 29 Topic 28 15189 non-null float64 30 Topic 29 15189 non-null float64 31 Topic 30 15189 non-null float64 dtypes: float64(30), object(2) memory usage: 3.7+ MB time: 11.2 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
doc_topic_df[['file_path']+topic_names].head(3).T
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
| 0 | 1 | 2 | |
|---|---|---|---|
| file_path | /Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf | /Users/farukcankaya/Google Drive/AllPapers/8_AH/140_AH.pdf | /Users/farukcankaya/Google Drive/AllPapers/8_AH/611_AH.pdf |
| Topic 1 | 0.0224593 | 0.0943667 | 5.49149e-05 |
| Topic 2 | 0.0192347 | 0.0193603 | 5.49149e-05 |
| Topic 3 | 5.67859e-05 | 0.186466 | 5.49149e-05 |
| Topic 4 | 0.0704275 | 0.042415 | 5.49149e-05 |
| Topic 5 | 5.67859e-05 | 0.211188 | 5.49149e-05 |
| Topic 6 | 0.170386 | 0.00580384 | 5.49149e-05 |
| Topic 7 | 5.67859e-05 | 0.0396027 | 5.49149e-05 |
| Topic 8 | 5.67859e-05 | 7.97448e-05 | 0.0104253 |
| Topic 9 | 0.22931 | 0.00499361 | 0.0274991 |
| Topic 10 | 5.67859e-05 | 0.0216886 | 5.49149e-05 |
| Topic 11 | 0.2198 | 0.0348093 | 5.49149e-05 |
| Topic 12 | 5.67859e-05 | 7.97448e-05 | 5.49149e-05 |
| Topic 13 | 0.0345742 | 7.97448e-05 | 5.49149e-05 |
| Topic 14 | 5.67859e-05 | 0.0476662 | 0.0337928 |
| Topic 15 | 5.67859e-05 | 7.97448e-05 | 0.273905 |
| Topic 16 | 5.67859e-05 | 7.97448e-05 | 5.49149e-05 |
| Topic 17 | 5.67859e-05 | 7.97448e-05 | 0.375401 |
| Topic 18 | 5.67859e-05 | 7.97448e-05 | 5.49149e-05 |
| Topic 19 | 5.67859e-05 | 7.97448e-05 | 5.49149e-05 |
| Topic 20 | 5.67859e-05 | 7.97448e-05 | 5.49149e-05 |
| Topic 21 | 0.150271 | 7.97448e-05 | 5.49149e-05 |
| Topic 22 | 5.67859e-05 | 0.011381 | 0.029833 |
| Topic 23 | 0.0180922 | 7.97448e-05 | 0.0162962 |
| Topic 24 | 0.0532823 | 7.97448e-05 | 5.49149e-05 |
| Topic 25 | 5.67859e-05 | 7.97448e-05 | 5.49149e-05 |
| Topic 26 | 5.67859e-05 | 0.120716 | 0.0255526 |
| Topic 27 | 5.67859e-05 | 0.146065 | 5.49149e-05 |
| Topic 28 | 5.67859e-05 | 7.97448e-05 | 0.178267 |
| Topic 29 | 0.0110846 | 0.0122819 | 0.0110525 |
| Topic 30 | 5.67859e-05 | 7.97448e-05 | 0.016932 |
time: 14.2 ms
topic_array = np.array(doc_topic_df[topic_names])
print(topic_array.shape)
print(topic_array[0])
(15189, 30) [2.24592851e-02 1.92347132e-02 5.67859171e-05 7.04274706e-02 5.67859171e-05 1.70385560e-01 5.67859171e-05 5.67859171e-05 2.29310066e-01 5.67859171e-05 2.19799982e-01 5.67859171e-05 3.45742082e-02 5.67859171e-05 5.67859171e-05 5.67859171e-05 5.67859171e-05 5.67859171e-05 5.67859171e-05 5.67859171e-05 1.50270688e-01 5.67859171e-05 1.80921556e-02 5.32823053e-02 5.67859171e-05 5.67859171e-05 5.67859171e-05 5.67859171e-05 1.10846337e-02 5.67859171e-05] time: 8.04 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
Save the model to use as pre-trained model on the https://simple-recommender.herokuapp.com/ website.
import pickle
topic_names = []
for i in range(NUM_OF_TOPICS):
topic_names.append("Topic {}".format(i+1))
topic_array = np.array(doc_topic_df[topic_names])
norms = np.linalg.norm(topic_array, axis = 1)
pickle.dump( norms, open( "model__norms.pkl", "wb" ) )
pickle.dump( topic_array, open( "model__topic_array.pkl", "wb" ) )
df_topic_keywords.to_pickle('model__df_topic_keywords.pkl')
pickle.dump( doc_topic_df, open( "model__doc_topic_df.pkl", "wb" ) )
pickle.dump( lda_model, open( "model__lda_model.pkl", "wb" ) )
pickle.dump( count_vectorizer, open( "model__count_vectorizer.pkl", "wb" ) )
pickle.dump( stop_list, open( "model__stop_list.pkl", "wb" ) )
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 360 ms
Use useful data from RELAVENT_DATA.xlsx
x = dt3c.copy()
# Paper Name, Author Name(s), Year and Journal
print(x['date'].isna().sum())
x['date'] = x['date'].fillna(0)
print(x['date'].isna().sum())
doc_topic_df['year'] = x['date']
213 0 time: 8.57 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def combine_authors(author_list):
if isinstance(author_list, list):
return ', '.join(author_list)
return author_list
x['Author'] = x['Author'].fillna('-')
x['authors'] = x['Author'].apply(combine_authors)
doc_topic_df['authors'] = x['authors']
time: 8.38 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
print(x['title'].isna().sum())
x['title'] = x['title'].fillna('-')
print(x['title'].isna().sum())
doc_topic_df['title'] = x['title']
1646 0 time: 4.62 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def journal_name(text):
text = get_file_name(text)
t = re.findall('([0-9]+)_([a-zA-Z]+)', text, re.IGNORECASE)
return [i[1] for i in t][0]
doc_topic_df['journal'] = x['file_path'].apply(journal_name)
doc_topic_df['journal'].head(5)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
0 AH 1 AH 2 AH 3 AH 4 AH Name: journal, dtype: object
time: 61.1 ms
rt = pd.read_excel('/Users/farukcankaya/FC/GoogleDrive/DATA_RELEVANT.xlsx')
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/xlrd/xlsx.py:266: DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/xlrd/xlsx.py:312: DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/xlrd/xlsx.py:266: DeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
time: 9.04 s
rt.iloc[rt.index[rt['Filename'].str.match(get_file_name(dt['file_path'][0]))][0]]['Title']
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
'The effect of system characteristics, stage of adoption, and experience on institutional explanations for ERP systems choice'
time: 15.5 ms
titles = []
for i in range(0, len(dt3c['file_path'])):
if rt['Filename'].str.match(get_file_name(dt3c['file_path'][i])).any():
titles.append(rt.iloc[rt.index[rt['Filename'].str.match(get_file_name(dt3c['file_path'][i]))][0]]['Title'])
else:
titles.append(dt3c['title'][i])
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 2min 35s
pickle.dump( doc_topic_df, open( "model__doc_topic_df.pkl", "wb" ) )
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
time: 228 ms
# LOAD pre-trained model if you just want to make prediction here.
"""
norms = pickle.load( open( "model__norms.pkl", "rb" ) )
topic_array = pickle.load( open( "model__topic_array.pkl", "rb" ) )
df_topic_keywords = pickle.load( open( "model__df_topic_keywords.pkl", "rb" ) )
doc_topic_df = pickle.load( open( "model__doc_topic_df.pkl", "rb" ) )
lda_model = pickle.load( open( "model__lda_model.pkl", "rb" ) )
count_vectorizer = pickle.load( open( "model__count_vectorizer.pkl", "rb" ) )
stop_list = pickle.load( open( "model__stop_list.pkl", "rb" ) )
"""
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
'\nnorms = pickle.load( open( "model__norms.pkl", "rb" ) )\ntopic_array = pickle.load( open( "model__topic_array.pkl", "rb" ) )\ndf_topic_keywords = pickle.load( open( "model__df_topic_keywords.pkl", "rb" ) )\ndoc_topic_df = pickle.load( open( "model__doc_topic_df.pkl", "rb" ) )\nlda_model = pickle.load( open( "model__lda_model.pkl", "rb" ) )\ncount_vectorizer = pickle.load( open( "model__count_vectorizer.pkl", "rb" ) )\nstop_list = pickle.load( open( "model__stop_list.pkl", "rb" ) )\n'
time: 2.98 ms
def compute_dists(top_vec, topic_array):
'''
Returns cosine distances for top_vec compared to every article
'''
dots = np.matmul(topic_array, top_vec)
input_norm = np.linalg.norm(top_vec)
co_dists = dots / (input_norm * norms)
return co_dists
def produce_rec(top_vec, topic_array, doc_topic_df, rand = 15):
'''
Produces a recommendation based on cosine distance.
rand controls magnitude of randomness.
'''
# top_vec = top_vec + np.random.rand(30,)/(np.linalg.norm(top_vec)) * rand
co_dists = compute_dists(top_vec, topic_array)
return doc_topic_df.loc[np.argmax(co_dists)]
time: 598 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
def sent_to_words(sentences):
for sentence in sentences:
yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))
# Define function to predict topic for a given new question.
def predict_topic(text, nlp=nlp):
global sent_to_words
# Clean
for i in range(len(text)):
text[i] = clean_text(text[i])
# Remove stopwords
for i in range(len(text)):
text[i] = remove_stopwords(text[i])
# Clean with simple_preprocess
mytext_2 = list(sent_to_words(text))
# Lemmatize is skipped !!!
# mytext_3_1 = []
# for sent in mytext_2:
# mytext_3_1.append(" ".join(sent))
# Lemmatize
mytext_3 = lemmatization(mytext_2, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])
#print('Text after lemmatization ({} characters):'.format(len(mytext_3[0])))
#print(mytext_3[0][0:100]+" ...")
# stemming is skipped !!!
#mytext_3_1 = []
#for t in mytext_3:
# mytext_3_1.append(stem_text(t))
# Vectorize transform
mytext_4 = count_vectorizer.transform(mytext_3)
#print('Count vectorizer of transformed text: {}'.format(mytext_4.shape))
#print('Vectorized data {}'.format(data_vectorized.shape))
#print('First 100 Feature names:')
#print(count_vectorizer.get_feature_names()[:100])
#print('')
# LDA Transform
topic_probability_scores = lda_model.transform(mytext_4)
topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), :].values.tolist()
return topic, topic_probability_scores
time: 961 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
# Predict the topic
TAR_6 = ["We examine how subjective performance evaluations are influenced by the level and controllability of an accompanying measure of a separate performance dimension. In our experiment, supervisors evaluate the office administration perfor- mance of a hypothetical subordinate. We find that supervisors’ subjective evaluations are directionally influenced by an accompanying objective measure of sales perfor- mance, even after excluding participants who perceive informativeness across measures. Consistent with concerns for fairness and motivation, we also find an asymmetric uncontrollability effect—supervisors’ evaluations are higher when an uncontrollable factor decreases the subordinate’s sales (i.e., they compensate for bad luck), but are not lower when the uncontrollable factor increases the subordinate’s sales (i.e., they do not punish for good luck). This evidence suggests that supervisors use discretion provided to evaluate performance on one task to adjust for perceived deficiencies in the evaluation of performance on other tasks. Our study integrates theories of cognitive bias and motivation, highlighting the need to consider the potentially interactive effects of different performance measures in multi-task settings I. INTRODUCTION Organizational incentive systems often allow managerial discretion in the evaluation of employee performance (Murphy and Oyer 2003). Subjective performance evaluation allows managers to use noncontractible information to assess actions and efforts that objective measures (such as those produced by the accounting system) are not able to capture, creating a more complete depiction of employee performance (Bol 2008).1 Subjectivity can therefore be useful in reducing risk to employees and improving the incentive alignment of the firm’s performance measurement system (Baker et al. 1994; Bushman et al. 1996; Hayes and Schaefer 2000).2 Many performance measurement systems include both objective measures and subjective evaluations (Prendergast 1999; Gibbs et al. 2004). While designed to capture separate dimensions of employee performance, these different measurement types can influence each other. For example, theories from psychology and organizational behavior suggest that subjective judgments can be unduly influenced by an individual’s knowledge of other, unrelated information (Nisbett et al. 1981; Bond et al. 2007). Such an influence could limit the complementary role of subjective evaluation in improving the overall informativeness of the measures used to evaluate performance. We examine how supervisors’ subjective performance evaluations are affected by the level and controllability of an objective measure of a separate aspect of performance. We analyze a two-dimensional employment setting in which an employee’s performance on one dimension (i.e., task) is measured objectively, while performance on the other dimension is evaluated subjectively by the supervisor. In many such settings, the objective performance measure is known by the supervisor before s/he subjectively evaluates the employee’s performance on the other dimension (Huber et al. 1987; Bommer et al. 1995). Our first research question examines whether the level of the objective measure has a directional impact on the supervisor’s subjective evaluation. Specifically, we examine whether, consistent with cognitive distortion, supervisors bias their subjective evaluations of performance on one dimension to be consistent with an objective measure of performance on a separate and unrelated dimension.3 Our second research question considers how such a spillover effect differs when the controllability of the objective performance measure is relatively low. Uncontrollability introduces noise and error into the performance measurement system (Feltham and Xie 1994), and has been shown to affect attribution judgments (Tan and Lipe 1997). Employees are likely to perceive uncontrollable performance measures to be unfair when the uncontrollability reduces measured performance. Perceptions of unfairness in compensation lead to reduced job satisfaction and motivation (Cohen-Charash and Spector 2001; Colquitt et al. 2001). We examine whether this expectation is reflected in supervisors’ use of their discretion, even when that discretion is provided for the evaluation of a separate task. Specifically, we examine whether supervisors use the discretion in their subjective evaluations to adjust for (or offset) the effects of an uncontrollable objective measure. Consistent with concerns for fairness and employee motivation, we predict that supervisors will make such an adjustment when the uncontrollable factor reduces measured performance (i.e., the employee suffers from bad luck) but not when it increases measured performance (i.e., the employee benefits from good luck). In our experiment, experienced supervisors employed by a large state university participate as evaluators in a hypothetical case setting. Participants assume the role of a regional director with supervisory authority over district managers who have both sales- and office administration-related duties. Objective individual sales information is given, after which participants are charged with subjectively evaluating the office administration performance of one district manager based on personal notes and staff interview responses, which are held constant in all conditions. The experiment employs a 2 3 2 þ 1 between-subjects design. In four treatment conditions, we manipulate the level of the manager’s individual sales at two levels by varying the objective sales score (high and low), and we manipulate the controllability of the individual sales measure at two levels by varying whether significant but uncontrollable events impacted the manager’s sales during the period. A fifth (control) condition includes only the information about the manager’s office administration performance. We find that supervisors’ subjective evaluations of the manager’s office administration performance are significantly higher (lower) when the objective level of the manager’s individual sales measure is relatively high (low). By excluding those participants who perceived the individual sales measure to be informative about the manager’s office administration performance in our primary analysis, we provide evidence of cognitive distortion of performance information as a result of exposure to the objective measure. We also find that the effect of the objective measure depends on its controllability. Specifically, participants use their subjective evaluations to adjust for the impact of uncontrollable events on the sales measure. Consistent with theory, this effect is asymmetric—participants’ evaluations are higher (relative to the high controllability condition) when the uncontrollable factor leads to an unfavorable outcome, but are no lower when the uncontrollable factor leads to a favorable outcome. Thus, we find that participants use their discretion to compensate for bad luck, but not to punish for good luck. This study contributes to the accounting and management literatures on performance evaluation. Prior research on bias in performance evaluation has found that supervisors’ subjective evaluations of current performance may be directionally influenced by prior performance information (Murphy et al. 1985; Huber et al. 1987; Kravitz and Balzer 1992) or information from different sources (Blakely 1993; Murphy and Cleveland 1995; Bono and Colbert 2005). Our study contributes to this line of research by showing that cognitive distortion can cause spillover from the objective evaluation of one dimension of performance to the subjective evaluation of performance on a separate and unrelated dimension. Prior research has also found evidence that controllability concerns can affect evaluators’ judgments in single-dimension performance-evaluation settings. For example, Tan and Lipe (1997) find that controllability moderates outcome effects in evaluators’ judgments (also see Brown and Solomon 1987). Our study reveals a very different controllability effect. Specifically, our results suggest that supervisors can use discretion that is provided for the evaluation of performance on a particular dimension to ‘‘correct’’ perceived deficiencies in other areas of the evaluation system. While the use of ‘‘borrowed’’ discretion can yield motivational benefits for employees, the intended benefits of that discretion (i.e., accurate reflection of performance on a specific dimension) can be reduced. Overall, our evidence indicates that the known level of an objective performance measure compromises the effectiveness of subjective performance evaluation for completing the picture of employee performance. Thus, our results have implications for the weight, timing, and nature of subjectivity in compensation contracting, suggesting that consideration of different measurement types in isolation can lead to incomplete conclusions about the optimal design of performance measurement systems. An asymmetric pattern of compensation payouts, in which managers are sheltered from downside risk but are allowed the advantages of upside risk, has been demonstrated in accounting"]
AOS_3 = ["This paper is focused on the way in which inter®rm relations can be drawn up and controlled. Drawing on con- tracting theories a model is built of the management control structure of inter®rm relationships. The model consists of three control patterns and of contingency factors that in ̄uence the choices between the patterns. Particular attention is paid to the role of trust. In building our model we take the perspective of the outsourcer. We try to re®ne our model by conducting case research in the ®eld of industrial renovation and industrial maintenance. In this ®eld we observed changes in the way outsourcing relationships are controlled. Case research can be very helpful for gaining insights into factors that in ̄uence the establishment of control structures of inter®rm relationships. By comparing the results of our case research we found institutional, strategic, cultural and historical factors to be of great importance for these control structures. 5 1999 Elsevier Science Ltd. All rights reserved. The boundaries of ®rms are continuously sub- ject to changes. Firms add new activities, hive o existing activities and set up new forms of coop- eration with other ®rms. The concentration on core activities is a tendency that already started several years ago. Non-core activities are hived o, so that the ®rm can concentrate on the activ- ities which it can perform well and which can thus produce the highest added value. Insight into which activities belong to the non-core activities is subject to changes too. We observe that conglom- erates with a great variety of products hive o complete product lines, but we also ®nd that ®rms, although they do not change their product mix, leave the production of components to other ®rms. We can also observe that ®rms outsource the provision of supporting services even more. Even services which are essential for the continuity of the ®rm, such as administration, information technology and maintenance. When the out- sourcing concerns essential components and ser- vices we observe the setting up of close forms of cooperation between ®rms. Hiving o essential components and services requires a speci®c embeddedness of the outsourcing relation in order to guarantee its continuity and to retain the eco- nomic advantages of outsourcing in the long run. This makes demands on the macro as well as micro level. On the macro level these develop- ments must be supported by legal and institutional frameworks (such as contract law and the attitude of branch organisations and the trade unions). On the micro level they make demands on the orga- nisation of the ®rm's own activities, the coordina- tion with the suppliers and the employees' willingness to work closely together with people from outside their own ®rm. The structuring and control of inter®rm relations require the establish- ment of suitable management control systems and processes. This study is in particular aimed at the demands outsourcing relations make on the micro level. The question is: how can ®rms which enter into strategic alliances with other ®rms structure the management control of such inter®rm relationships? Management control and management accounting research usually concentrates on rela- tions within organisations with varying degrees of decentralisation of decision making. Robert Anthony's (1965, 1988) well-known framework in which the decomposition of the organisation into responsibility centers is paramount, is the implicit or explicit point of departure for a lot of formal behavioural and contingency research in manage- ment control. In numerous organisations a hor- izontalisation of management control relations can be observed. These control relations are not hierarchically based but are relations between equal internal parties. Customer-supplier relation systems are being set up with forms of internal contracting (service level agreements), e.g. with respect to facility services (Van der Meer-Kooistra, 1994; Vosselman, 1996). There used to be captive buyers and sellers, but now it is no longer excep- tional for departments to be allowed to deliver or to buy on external markets within rather ̄exible limits. Otley (1994) too observes such a horizontalisation and proposes a broader management control fra- mework. Research into horizontal management control relations should not remain restricted to intra®rm relations, but be extended to inter®rm relations. Recently Hopwood urged ``(the) exam- ination of some of the accounting and informational consequences of more explicit concerns with the management of the supply chain and a more con- scious questioning of what activities reside within and without the enterprise'' (Hopwood, 1996, p. 590). In the relevant literature little attention is given to management control issues with regard to forms of cooperation between independent com- panies (inter®rm relations). It is in particular the strategic cost management literature (Cooper, 1995; Shank & Govindarajan, 1993) which draws attention to the importance of looking beyond the boundaries of the ®rm. These publications emphasise the value chain approach, which not only focuses on the relations between the various subsequent activities within the ®rm but also on the ®rm's relations with the suppliers of raw materials, components etc. and the buyers. Berry (1995) also focuses on extra-organisational control problems, in particular control in network orga- nisations. According to Gietzmann (1996) the role of the management accountant is changing due to control beyond the boundaries of the concern. When deciding on what should or should not be contracted out this role is much more complex than just calculating the production costs and comparing them with the market price. Make or buy decisions are becoming more and more of a strategic nature (see e.g. Tayles & Drury, 1997). ``The management accountant should play a cri- tical support role in the strategy debate concerning which elements (subcomponents) of product development can be outsourced, which should be developed in house and which do not warrant development since o the shelf subcomponents suce'' (Gietzmann, p. 624). Therefore the man- agement accountant needs not only information about the production costs and market prices but he also needs information about the costs and bene®ts of the speci®c forms of governance connected with these choices. Nowadays many make or buy decisions have evolved into what could be called `extended make or buy' decisions. An extended make or buy deci- sion can be divided into three separate, but inter- related elements. Firstly, the decision to purchase goods or services on a structural basis from third parties or to produce the goods or services in- house. Secondly, in the case of purchasing from third parties, the decision regarding the party or parties contracted. The nature of the party or parties in ̄uences the chance, and extent, of relational risks. Thirdly, decisions regarding the design of the internal management control system or the design of the outsourcing relationship."]
topic, prob_scores = predict_topic(text = TAR_6)
print('topic')
print(topic)
print('')
print('prob_scores')
print(prob_scores[0])
print("shape of prob_scores: {}".format(prob_scores[0].shape))
print('')
print("Try to make recommendation. Inputs:")
print("- prob_scores")
print("- topic_array: {} prob. distribution of topics for each document that is calculated by LDA".format(topic_array.shape))
print("- doc_topic_df: {} that is actually output of the LDA but combined with file_path, etc.".format(doc_topic_df.shape))
print('')
produce_rec(prob_scores.flatten(), topic_array, doc_topic_df)
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
topic ['performance', 'management', 'control', 'cost', 'study', 'organization', 'measure', 'strategy', 'manager', 'organizational', 'strategic', 'process', 'decision', 'information', 'base', 'resource', 'customer', 'activity', 'relationship', 'level'] prob_scores [6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.21893821e-01 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.21506031e-01 3.64299895e-01 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.66056941e-03 2.66054459e-03 3.78559184e-03 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.32281151e-02 6.25390869e-05 1.36969279e-01 6.25390869e-05 2.16828311e-02] shape of prob_scores: (30,) Try to make recommendation. Inputs: - prob_scores - topic_array: (15189, 30) prob. distribution of topics for each document that is calculated by LDA - doc_topic_df: (15189, 36) that is actually output of the LDA but combined with file_path, etc.
file_path /Users/farukcankaya/Google Drive/AllPapers/1_TAR/6_TAR.pdf content_3 effect subjective performance evaluation controllability subjective performance evaluation influence level controllability accompany measure separate performance dimension experiment supervisor evaluate subordinate supervisor subjective evaluation directionally influence accompany objective measure exclude participant perceive informativeness measure consistent concern uncontrollability effectsupervisor evaluation higher uncontrollable factor decrease subordinate compensate bad uncontrollable factor increase subordinate punish evidence suggest supervisor discretion provide performance task perceive deficiency evaluation performance task study integrate theory cognitive motivation highlight potentially interactive effect performance measure multi task setting keyword subjective performance evaluation availability contact author helpful comment suggestion midyear meeting birkey valuable research assistance research fund editor note accept submit accept publish online introduction rganizational incentive system managerial discretion evaluation employee performance oyer subjective performance evaluation manager noncontractible information assess action effort objective measure produce accounting capture create complete depiction employee performance subjectivity reduce risk employee improve incentive alignment firm performance system include objective measure subjective evaluation design capture separate dimension employee performance measurement type influence theory psychology organizational behavior suggest subjective judgment unduly influence individual knowledge unrelated information limit complementary role subjective evaluation improve informativeness measure evaluate performance supervisor subjective performance evaluation affect level controllability objective measure analyze dimensional employment set employee performance dimension task measure objectively performance dimension evaluate subjectively supervisor setting objective performance measure supervisor subjectively evaluate employee performance dimension research question examine level objective measure directional impact supervisor subjective evaluation specifically examine consistent cognitive distortion supervisor subjective evaluation performance dimension consistent objective measure performance separate unrelated dimension research question consider effect differ objective performance measure uncontrollability introduce noise attribution judgment lipe employee perceive uncontrollable performance measure unfair uncontrollability reduce measure performance perception reduce job satisfaction motivation spector expectation reflect supervisor discretion discretion provide evaluation separate task specifically examine supervisor discretion subjective evaluation adjust offset effect uncontrollable objective measure consistent concern fairness employee motivation predict define subjectivity judgment base personal impression opinion verifiable court noncontractible subjectivity performance evaluation introduce subjective performance measure allow flexibility weight objective performance measure allow discretional adjustment base factor performance measure ante mechanism supervisor subjective evaluation influence subjective measure objective measure specifically examine effect objective performance measure measure subjective evaluation perform interactive effect controllability feature objective performance measure accounting review supervisor adjustment uncontrollable factor reduce measure performance employee suffer bad increase measure performance employee benefit hypothetical setting participant assume role regional director supervisory authority administration duty objective individual information participant charge subjectively evaluate performance manager base personal note staff interview response experiment employ subject design treatment condition manipulate level manager individual level vary objective score manipulate controllability individual measure level vary significant uncontrollable event impact manager period control condition include information performance supervisor subjective evaluation performance significantly high objective level manager individual measure exclude participant perceive individual measure informative performance primary analysis provide evidence cognitive distortion performance information result objective measure effect objective measure depend controllability specifically participant subjective evaluation adjust impact measure consistent theory effect asymmetricparticipant evaluation high relative controllability condition uncontrollable factor lead unfavorable outcome uncontrollable factor lead favorable outcome participant discretion compensate bad punish study contribute accounting management literature performance evaluation research performance evaluation supervisor subjective evaluation current performance directionally influence performance information information source study contribute line research show cognitive distortion spillover objective evaluation dimension performance subjective evaluation performance separate unrelated dimension research evidence controllability concern affect evaluator judgment single dimension performance evaluation setting controllability moderate reveal controllability effect specifically result suggest supervisor discretion provide evaluation performance dimension area evaluation borrow discretion yield motivational benefit employee intend benefit discretion accurate reflection performance specific dimension reduce evidence level objective performance measure compromise effectiveness subjective performance evaluation complete picture employee performance result implication weight timing nature subjectivity compensation contracting suggest consideration measurement type isolation lead incomplete conclusion design performance measurement pattern compensation payout manager shelter downside risk allow advantage upside risk demonstrate accounting spillover effect subjective performance evaluation accounting review Topic 1 5.5371e-05 Topic 2 5.5371e-05 Topic 3 5.5371e-05 Topic 4 5.5371e-05 Topic 5 5.5371e-05 Topic 6 0.2158 Topic 7 5.5371e-05 Topic 8 5.5371e-05 Topic 9 5.5371e-05 Topic 10 0.212073 Topic 11 0.372352 Topic 12 5.5371e-05 Topic 13 5.5371e-05 Topic 14 5.5371e-05 Topic 15 5.5371e-05 Topic 16 5.5371e-05 Topic 17 5.5371e-05 Topic 18 0.00241411 Topic 19 0.00237663 Topic 20 0.00327981 Topic 21 5.5371e-05 Topic 22 5.5371e-05 Topic 23 5.5371e-05 Topic 24 5.5371e-05 Topic 25 5.5371e-05 Topic 26 0.0485006 Topic 27 5.5371e-05 Topic 28 0.128912 Topic 29 5.5371e-05 Topic 30 0.0131305 year 2019-04-03T19:47:28Z authors Jasmijn C.Bol and Steven D.Smith title Spillover Effects in Subjective Performance Evaluation: Bias and the Asymmetric Influence of Controllability journal TAR Name: 7630, dtype: object
time: 311 ms
from sklearn.metrics.pairwise import cosine_similarity
def produce_rec_top_n(top_vec, topic_array, doc_topic_df, n=5):
co_dists = compute_dists(top_vec, topic_array)
index_top_n = np.argpartition(co_dists, -n)[-n:]
index_top_n = index_top_n[np.argsort(-co_dists[index_top_n])]
return doc_topic_df.loc[index_top_n]
topic, prob_scores = predict_topic(text = TAR_6)
topic_columns = ['Topic '+str(i) for i in range(1,df_topic_keywords.shape[0]+1)]
recs = produce_rec_top_n(prob_scores.flatten(), topic_array, doc_topic_df)
rec_top_1 = recs[topic_columns].head(1).values
print('rec_top_1:')
print(rec_top_1)
print('prob_scores:')
print(prob_scores.flatten())
print('cosine_similarity:')
print(cosine_similarity(rec_top_1,prob_scores))
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
rec_top_1: [[5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 2.15799817e-01 5.53709856e-05 5.53709856e-05 5.53709856e-05 2.12072575e-01 3.72351548e-01 5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 2.41411018e-03 2.37663249e-03 3.27981187e-03 5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 5.53709856e-05 4.85005919e-02 5.53709856e-05 1.28911613e-01 5.53709856e-05 1.31305095e-02]] prob_scores: [6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.21893821e-01 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.21506031e-01 3.64299895e-01 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.66056941e-03 2.66054459e-03 3.78559184e-03 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 6.25390869e-05 2.32281151e-02 6.25390869e-05 1.36969279e-01 6.25390869e-05 2.16828311e-02] cosine_similarity: [[0.99806867]] time: 242 ms
recs['file_path']
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
7630 /Users/farukcankaya/Google Drive/AllPapers/1_TAR/6_TAR.pdf 11458 /Users/farukcankaya/Google Drive/AllPapers/4_AOS/103_AOS.pdf 10945 /Users/farukcankaya/Google Drive/AllPapers/4_AOS/808_AOS.pdf 11320 /Users/farukcankaya/Google Drive/AllPapers/4_AOS/757_AOS.pdf 5071 /Users/farukcankaya/Google Drive/AllPapers/5_CAR/524_CAR.pdf Name: file_path, dtype: object
time: 4.1 ms
def accuracy_n(file_path, data, top_n=[1]):
topic, prob_scores = predict_topic([data])
topic_columns = ['Topic '+str(i) for i in range(1,df_topic_keywords.shape[0]+1)]
recs = produce_rec_top_n(prob_scores.flatten(), topic_array, doc_topic_df, max(top_n))
existence = []
for n in top_n:
if recs['file_path'].head(n).str.contains(get_file_name(file_path)).any():
existence.append(1)
else:
existence.append(0)
return pd.Series(existence)
time: 754 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
#dt3c['file_path'].apply().head(1)
#get_file_name('/Users/farukcankaya/Google Drive/AllPapers/8_AH/711_AH.pdf')
#dt3c['file_path'].parallel_apply(get_file_name).str.match('711_AH').head(1)
time: 343 µs
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
#papers_has_10_words.index
xx = dt.copy().drop(unknown_lang_indexes)
print(xx.info())
xx.reset_index(drop=True, inplace=True)
print(xx.info())
non_normalized_indexes = []
yy = dt3c['file_path'].parallel_apply(get_file_name)
for i in range(0, len(xx['file_path'])):
if not yy.str.match(get_file_name(xx['file_path'][i])).any():
non_normalized_indexes.append(i)
print(x.info())
xx = xx.drop(non_normalized_indexes)
xx.reset_index(drop=True, inplace=True)
xx.info()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
<class 'pandas.core.frame.DataFrame'> Int64Index: 15189 entries, 0 to 15750 Columns: 3435 entries, file_path to en_cleaned_accounting_replaced_stop_words_lemmatized_page_330 dtypes: float64(330), int64(337), object(2768) memory usage: 398.2+ MB None <class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Columns: 3435 entries, file_path to en_cleaned_accounting_replaced_stop_words_lemmatized_page_330 dtypes: float64(330), int64(337), object(2768) memory usage: 398.1+ MB None <class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 file_path 15189 non-null object 1 Author 15189 non-null object 2 date 15189 non-null object 3 title 15189 non-null object 4 content_3 15189 non-null object 5 content_5 15189 non-null object 6 content_10 15189 non-null object 7 content_3_lang 15189 non-null object 8 content_5_lang 15189 non-null object 9 content_10_lang 15189 non-null object 10 authors 15189 non-null object dtypes: object(11) memory usage: 1.3+ MB None <class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Columns: 3435 entries, file_path to en_cleaned_accounting_replaced_stop_words_lemmatized_page_330 dtypes: float64(330), int64(337), object(2768) memory usage: 398.1+ MB time: 2min 51s
## PARALLEL TEST
#xx = dt.copy().drop(unknown_lang_indexes)
top_n = [1, 5, 10, 20, 50, 100]
col_acc_n = ['acc_top_'+str(n) for n in top_n]
xx[col_acc_n] = xx.parallel_apply(lambda x: accuracy_n(x['file_path'], x['page_1'], top_n), axis=1)
xx.info()
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1 /Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/sklearn/decomposition/_lda.py:390: UserWarning: Loky-backed parallel loops cannot be called in a multiprocessing, setting n_jobs=1
<class 'pandas.core.frame.DataFrame'> RangeIndex: 15189 entries, 0 to 15188 Columns: 3435 entries, file_path to en_cleaned_accounting_replaced_stop_words_lemmatized_page_330 dtypes: float64(330), int64(337), object(2768) memory usage: 398.1+ MB time: 9min 46s
for n in top_n:
print('Top-'+str(n)+' Score:' + str(xx['acc_top_'+str(n)].sum()/xx['acc_top_'+str(n)].count()))
Top-1 Score:0.4058200013167424 Top-5 Score:0.6152478767529133 Top-10 Score:0.6965567186779906 Top-20 Score:0.7691092237803674 Top-50 Score:0.8529198762262163 Top-100 Score:0.9011126473105537 time: 7.09 ms
/Users/farukcankaya/FC/TUM/IDP/nlp-examples/.venv/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.